Abstract

Nominal predicates often carry implicit arguments. Recent work on semantic role labeling has focused on identifying arguments within the local context of a predicate; implicit arguments, however, have not been systematically examined. To address this limitation, we have manually annotated a corpus of implicit arguments for ten predicates from NomBank. Through analysis of this corpus, we find that implicit arguments add 71% to the argument structures that are present in NomBank. Using the corpus, we train a discriminative model that is able to identify implicit arguments with an F1 score of 50%, significantly outperforming an informed baseline model. This article describes our investigation, explores a wide variety of features important for the task, and discusses future directions for work on implicit argument identification.

1. Introduction

Recent work has shown that semantic role labeling (SRL) can be applied to nominal predicates in much the same way as verbal predicates (Liu and Ng 2007; Johansson and Nugues 2008; Gerber, Chai, and Meyers 2009). In general, the nominal SRL problem is formulated as follows: Given a predicate that is annotated in NomBank as bearing arguments, identify these arguments within the clause or sentence that contains the predicate. As shown in our previous work (Gerber, Chai, and Meyers 2009), this problem definition ignores the important fact that many nominal predicates do not bear arguments in the local context. Such predicates need to be addressed in order for nominal SRL to be used by downstream applications such as automatic question answering, information extraction, and statistical machine translation.

Gerber, Chai, and Meyers (2009) showed that it is possible to accurately identify nominal predicates that bear arguments in the local context. This makes the nominal SRL system applicable to text that does not contain annotated predicates. The system does not address a fundamental question regarding arguments of nominal predicates, however: If an argument is missing from the local context of a predicate, might the argument be located somewhere in the wider discourse? Most prior work on nominal and verbal SRL has stopped short of answering this question, opting instead for an approach that only labels local arguments and thus ignores predicates whose arguments are entirely non-local. This article directly addresses the issue of non-local (or implicit) argument identification for nominal predicates.

As an initial example, consider the following sentence, which is taken from the Penn TreeBank (Marcus, Santorini, and Marcinkiewicz 1993):

  • (1) 

    A SEC proposal to ease [arg1 reporting] [predicate requirements] [arg2 for some company executives] would undermine the usefulness of information on insider trades, professional money managers contend.

The NomBank (Meyers 2007) role set for requirement is shown here:
  • Frame forrequirement, role set 1:

  • arg0: the entity that is requiring something

  • arg1: the entity that is required

  • arg2: the entity of which something is being required

In Example (1), the predicate has been annotated with the local argument labels provided by NomBank. As shown, NomBank does not annotate an arg0 for this instance of the requirement predicate; a reasonable interpretation of the sentence, however, is that SEC is the entity that is requiring something.1 This article refers to arguments such as SEC in Example (1) as implicit. In this work, the notion of implicit argument covers any argument that is not annotated by NomBank.2

Building on Example (1), consider the following sentence, which directly follows Example (1) in the corresponding TreeBank document:

  • (2) 

    Money managers make the argument in letters to the agency about [arg1 rule] [predicate changes] proposed this past summer.

The NomBank role set for change is as follows:
  • Frame forchange, role set 1:

  • arg0: the entity that initiates the change

  • arg1: the entity that is changed

  • arg2: the initial state of the changed entity

  • arg3: the final state of the changed entity

Similarly to the previous example, Example (2) shows the local argument labels provided by NomBank. These labels only indicate that rules have been changed. For a full interpretation, Example (2) requires an understanding of Example (1). Without the sentence from Example 1, the reader has no way of knowing that the agency in Example (2) actually refers to the same entity as SEC in Example (1). As part of the reader’s comprehension process, this entity is identified as the filler for the arg0 role in Example (2). This identification must occur in order for these two sentences to form a coherent discourse.

From these examples, it is clear that the scope of implicit arguments quite naturally spans sentence boundaries. Thus, if one wishes to recover implicit arguments as part of the SRL process, the argument search space must be expanded beyond the traditional, single-sentence window used in virtually all prior SRL research. What can we hope to gain from such a fundamental modification of the problem? Consider the following question, which targets Examples (1) and (2):

  • (3) 

    Who changed the rules regarding reporting requirements?

Question (3) is a factoid question, meaning it has a short, unambiguous answer in the targeted text. This type of question has been studied extensively in the Text Retrieval Conference Question Answering (QA) Track (Dang, Kelly, and Lin 2007). Using the evaluation data from this track, Pizzato and Mollá (2008) showed that SRL can improve the accuracy of a QA system; a traditional SRL system alone, however, is not enough to recover the implied answer to Question (3): SEC or the agency. Successful implicit argument identification provides the answer in this case.

This article presents an in-depth study of implicit arguments for nominal predicates.3 The following section surveys research related to implicit argument identification. Section 3 describes the study’s implicit argument annotation process and the data it produced. The implicit argument identification model is formulated in Section 4 and evaluated in Section 5. Discussion of results is provided in Section 6, and the article concludes in Section 7.

2. Related Work

The research presented in this article is related to a wide range of topics in cognitive science, linguistics, and natural language processing (NLP). This is partly due to the discourse-based nature of the problem. In single-sentence SRL, one can ignore the discourse aspect of language and still obtain high marks in an evaluation (for examples, see Carreras and Màrquez 2005 and Surdeanu et al. 2008); implicit argumentation, however, forces one to consider the discourse context in which a sentence exists. Much has been said about the importance of discourse to language understanding, and this section will identify the points most relevant to implicit argumentation.

2.1 Discourse Comprehension in Cognitive Science

The traditional view of sentence-level semantics has been that meaning is compositional. That is, one can derive the meaning of a sentence by carefully composing the meanings of its constituent parts (Heim and Kratzer 1998). There are counterexamples to a compositional theory of semantics (e.g., idioms), but those are more the exception than the rule. Things change, however, when one starts to group sentences together to form coherent textual discourses. Consider the following examples, borrowed from (1981, page 5):

  • (4) 

    Jill came bouncing down the stairs.

  • (5) 

    Harry rushed off to get the doctor.

Examples (4) and (5) describe three events: bounce, rush, and get. These events are intricately related. One cannot simply create a conjunction of the propositions bounce, rush, and get and expect to arrive at the author’s intended meaning, which presumably involves Jill’s becoming injured by her fall and Harry’s actions to help her. The mutual dependence of these sentences can be further shown by considering a variant of the situation described in Examples (4) and (5):
  • (6) 

    Jill came bouncing down the stairs.

  • (7) 

    Harry rushed over to kiss her.

The interpretation of Example (6) is vastly different from the interpretation of Example (4). In Example (4), Jill becomes injured whereas in Example (6) she is quite happy.

Examples (4–7) demonstrate the fact that sentences do not have a fixed, compositional interpretation; rather, a sentence’s interpretation depends on the surrounding context. The standard compositional theory of sentential semantics largely ignores contextual information provided by other sentences. The single-sentence approach to SRL operates similarly. In both of these methods, the current sentence provides all of the semantic information. In contrast to these methods—and aligned with the preceding discussion—this article presents methods that rely heavily on surrounding sentences to provide additional semantic information. This information is used to interpret the current sentence in a more complete fashion.

Examples (4–7) also show that the reader’s knowledge plays a key role in discourse comprehension. Researchers in cognitive science have proposed many models of reader knowledge. Schank and Abelson (1977) proposed stereotypical event sequences called scripts as a basis for discourse comprehension. In this approach, readers fill in a discourse’s semantic gaps with knowledge of how a typical event sequence might unfold. In Examples (4) and (5), the reader knows that people typically call on a doctor only if someone is hurt. Thus, the reader automatically fills the semantic gap caused by the ambiguous predicate bounce with information about doctors and what they do. Similar observations have been made by (1977, page 4), van Dijk and Kintsch (1983, page 303), Graesser and Clark (1985, page 14), and Carpenter, Miyake, and Just (1995). Inspired by these ideas, the model developed in this article relies partly on large text corpora, which are treated as repositories of typical event sequences. The model uses information extracted from these event sequences to identify implicit arguments.

2.2 Automatic Relation Discovery

Examples (4) and (5) in the previous section show that understanding the relationships between predicates is a key part of understanding a textual discourse. In this section, we review work on automatic predicate relationship discovery, which attempts to extract these relationships automatically.

Lin and Pantel (2001) proposed a system that automatically identifies relationships similar to the following:

  • (8) 

    XeatsYXlikesY

This relationship creates a mapping between the participants of the two predicates. One can imagine using such a mapping to fill in the semantic gaps of a discourse that describes a typical set of events in a restaurant. In such a discourse, the author probably will not state directly that X likes Y; the reader might need to infer this in order to make sense of the fact that X left a large tip for the waiter, however.

Lin and Pantel (2001) created mappings such as the one in Example (8) using a variation of the so-called “distributional hypothesis” posited by Harris (1985), which states that words occurring in similar contexts tend to have similar meanings. Lin and Pantel applied the same notion of similarity to dependency paths. For example, the inference rule in Example 8 is identified by examining the sets of words in the two X positions and the sets of words in the two Y positions. When the two pairs of sets are similar, it is implied that the two dependency paths from X to Y are similar as well. In Example (8), the two dependency paths are as follows:
formula
One drawback of this method is that it assumes the implication is symmetric. Although this assumption is correct in many cases, it often leads to invalid inferences. In Example 8, it is not always true that if X likes Y then X will eat Y. The opposite—that X eating Y implies X likes Y—is more plausible but not certain.

Bhagat, Pantel, and Hovy (2007) extended the work of Lin and Pantel (2001) to handle cases of asymmetric relationships. The basic idea proposed by Bhagat, Pantel, and Hovy is that, when considering a relationship of the form 〈x, p1, y〉 ↔ 〈x, p2, y〉, if p1 occurs in significantly more contexts (i.e., has more options for x and y) than p2, then p2 is likely to imply p1 but not vice versa. Returning to Example 8, we see that the correct implication will be derived if likes occurs in significantly more contexts than eats. The intuition is that the more general concept (i.e., like) will be associated with more contexts and is more likely to be implied by the specific concept (i.e., eat). As shown by Bhagat, Pantel, and Hovy, the system built around this intuition is able to effectively identify the directionality of many inference rules.

Zanzotto, Pennacchiotti, and Pazienza (2006) presented another study aimed at identifying asymmetric relationships between verbs. For example, the asymmetric entailment relationship XwinsXplays holds, but the opposite (XplaysXwins) does not. This is because not all those who play a game actually win. To find evidence for this automatically, the authors examined constructions such as the following (adapted from (Zanzotto, Pennacchiotti, and Pazienza [2006]):

  • (9) 

    The more experienced tennis player won the match.

The underlying idea behind the authors’ approach is that asymmetric relationships such as XwinsXplays are often entailed by constructions involving agentive, nominalized verbs as the logical subjects of the main verb. In Example (9), the agentive nominal “player” is logical subject to “won”, the combination of which entails the asymmetric relationship of interest. Thus, to validate such an asymmetric relationship, Zanzotto, Pennacchiotti, and Pazienza (2006) examined the frequency of the “player win” collocation using Google hit counts as a proxy for actual corpus statistics.

A number of other studies (e.g., Szpektor et al. 2004, Pantel et al. 2007) have been conducted that are similar to that work. In general, such work focuses on the automatic acquisition of entailment relationships between verbs. Although this work has often been motivated by the need for lexical–semantic information in tasks such as automatic question answering, it is also relevant to the task of implicit argument identification because the derived relationships implicitly encode a participant role mapping between two predicates. For example, given a missing arg0 for a like predicate and an explicit arg0 = John for an eat predicate in the preceding discourse, inference rule (8) would help identify the implicit arg0 = John for the like predicate.

The missing link between previous work on verb relationship identification and the task of implicit argument identification is that previous verb relations are not defined in terms of the argn positions used by NomBank. Rather, positions like subject and object are used. In order to identify implicit arguments in NomBank, one needs inference rules between specific argument positions (e.g., eat:arg0 and like:arg0). In the current article, we propose methods of automatically acquiring these fine-grained relationships for verbal and nominal predicates using existing corpora. We also propose a method of using these relationships to recover implicit arguments.

2.3 Coreference Resolution

The referent of a linguistic expression is the real or imagined entity to which the expression refers. Coreference, therefore, is the condition of two linguistic expressions having the same referent. In the following examples from the Penn TreeBank, the underlined spans of text are coreferential:

  • (10) 

    Carpet King sales are up 4% this year,” said owner .

  • (11) 

    added that the company has been manufacturing carpet since 1967.

Non-trivial instances of coreference (e.g., Carpet King and the company) allow the author to repeatedly mention the same entity without introducing redundancy into the discourse. Pronominal anaphora is a subset of coreference in which one of the referring expressions is a pronoun. For example, he in Example (11) refers to the same entity as Richard Rippe in Example (10). These examples demonstrate noun phrase coreference. Events, indicated by either verbal or nominal predicates, can also be coreferential when mentioned multiple times in a document (Wilson 1974; Chen and Ji 2009).

For many years, the Automatic Content Extraction (ACE) series of large-scale evaluations (NIST 2008) has provided a test environment for systems designed to identify these and other coreference relations. Systems based on the ACE data sets typically take a supervised learning approach to coreference resolution in general (Versley et al. 2008) and pronominal anaphor in particular (Yang, Su, and Tan 2008).

A phenomenon similar to the implicit argument has been studied in the context of Japanese anaphora resolution, where a missing case-marked constituent is viewed as a zero-anaphoric expression whose antecedent is treated as the implicit argument of the predicate of interest. This behavior has been annotated manually by Iida et al. (2007), and researchers have applied standard SRL techniques to this corpus, resulting in systems that are able to identify missing case–marked expressions in the surrounding discourse (Imamura, Saito, and Izumi 2009). Sasano, Kawahara, and Kurohashi (2004) conducted similar work with Japanese indirect anaphora. The authors used automatically derived nominal case frames to identify antecedents. However, as noted by Iida et al., grammatical cases do not stand in a one-to-one relationship with semantic roles in Japanese (the same is true for English).

Many other discourse-level phenomena interact with coreference. For example, Centering Theory (Grosz, Joshi, and Weinstein 1995) focuses on the ways in which referring expressions maintain (or break) coherence in a discourse. These so-called “centering shifts” result from a lack of coreference between salient noun phrases in adjacent sentences. Discourse Representation Theory (DRT) (Kamp and Reyle 1993) is another prominent treatment of referring expressions. DRT embeds a theory of coreference into a first-order, compositional semantics of discourse.

2.4 Identifying Implicit Arguments

Past research on the actual task of implicit argument identification tends to be sparse. Palmer et al. (1986) describe what appears to be the first computational treatment of implicit arguments. In that work, Palmer et al. manually created a repository of knowledge concerning entities in the domain of electronic device failures. This knowledge, along with hand-coded syntactic and semantic processing rules, allowed the system to identify implicit arguments across sentence boundaries. As a simple example, consider the following two sentences (borrowed from Palmer et al. [1986]):

  • (12) 

    Disk drive was down at 11/16-2305.

  • (13) 

    Has select lock.

Example (13) does not specify precisely which entity has select lock. The domain knowledge, however, tells the system that only disk drive entities can have such a property. Using this knowledge, the system is able to search the local context and make explicit the implied fact that the disk drive from Example (12) has select lock.

A similar line of work was pursued by Whittemore, Macpherson, and Carlson (1991), who offer the following example of implicit argumentation (page 21):

  • (14) 

    Pete bought a car.

  • (15) 

    The salesman was a real jerk.

In Example (14), the buy event is not associated with an entity representing the seller. This entity is introduced in Example (15) as the salesman, whose semantic properties satisfy the requirements of the buy event. Whittemore, Macpherson, and Carlson (1991) build up the event representation incrementally using a combination of semantic property constraints and DRT.

The systems developed by Palmer et al. (1986) and Whittemore, Macpherson, and Carlson (1991) are quite similar. They both make use of semantic constraints on arguments, otherwise known as selectional preferences. Selectional preferences have received a significant amount of attention over the years, with the work of Ritter, Mausam, and Etzioni (2010) being some of the most recent. The model developed in the current article uses a variety of selectional preference measures to identify implicit arguments.

The implicit argument identification systems described herein were not widely deployed due to their reliance on hand-coded, domain-specific knowledge that is difficult to create. Much of this knowledge targeted basic syntactic and semantic constructions that now have robust statistical models (e.g., those created by (Charniak and Johnson 2005) for syntax and (Punyakanok et al. 2005) for semantics). With this information accounted for, it is easier to approach the problem of implicit argumentation. Subsequently, we describe a series of recent investigations that have led to a surge of interest in statistical implicit argument identification.

Fillmore and Baker (2001) provided a detailed case study of FrameNet frames as a basis for understanding written text. In their case study, Fillmore and Baker manually build up a semantic discourse structure by hooking together frames from the various sentences. In doing so, the authors resolve some implicit arguments found in the discourse. This process is an interesting step forward; the authors did not provide concrete methods to perform the analysis automatically, however.

Nielsen (2004) developed a system that is able to detect the occurrence of verb phrase ellipsis. Consider the following sentences:

  • (16) 

    John kicked the ball.

  • (17) 

    Bill [did], too.

The bracketed text in Example (17) is a placeholder for the verb phrase kicked the ball in Example (16), which has been elided (i.e., left out). Thus, in Example (17), Bill can be thought of as an implicit argument to some kicking event that is not mentioned. If one resolved the verb phrase ellipsis, then the implicit agent (Bill) would be recovered.4 Nielsen (2004) created a system able to detect the presence of ellipses, producing the bracketing in Example (17). Ellipsis resolution (i.e., figuring out precisely which verb phrase is missing) was described by Nielsen (2005). Implicit argument identification for nominal predicates is complementary to verb phrase ellipsis resolution: Both work to make implicit information explicit.

Burchardt, Frank, and Pinkal (2005) suggested that frame elements from various frames in a text could be linked to form a coherent discourse interpretation (this is similar to the idea described by Fillmore and Baker [2001]). The linking operation causes two frame elements to be viewed as coreferent. Burchardt, Frank, and Pinkal (2005) propose to learn frame element linking patterns from observed data; the authors did not implement and evaluate such a method, however. Building on the work of Burchardt, Frank, and Pinkal, this article presents a model of implicit arguments that uses a quantitative analysis of naturally occurring coreference patterns.

In our previous work, we demonstrated the importance of filtering out nominal predicates that take no local arguments (Gerber, Chai, and Meyers 2009). This approach leads to appreciable gains for certain nominals. The approach does not attempt to actually recover implicit arguments, however.

Most recently, Ruppenhofer et al. (2009) proposed SemEval Task 10, “Linking Events and Their Participants in Discourse,” which evaluated implicit argument identification systems over a common test set. The task organizers annotated implicit arguments across entire passages, resulting in data that cover many distinct predicates, each associated with a small number of annotated instances. As described by Ruppenhofer et al. (2010), three submissions were made to the competition, with two of the submissions attempting the implicit argument identification part of the task. Chen et al. (2010) extended a standard SRL system by widening the candidate window to include constituents from other sentences. A small number of features based on the FrameNet frame definitions were extracted for these candidates, and prediction was performed using a log-linear model. Tonelli and Delmonte (2010) also extended a standard SRL system. Both of these systems achieved an implicit argument F1 score of less than 0.02. The organizers and participants appear to agree that training data sparseness was a significant problem. This is likely the result of the annotation methodology: Entire documents were annotated, causing each predicate to receive a very small number of annotated examples.

In contrast to the evaluation described by Ruppenhofer et al. (2010), the study presented in this article focused on a select group of nominal predicates. To help prevent data sparseness, the size of the group was small, and the predicates were carefully chosen to maximize the observed frequency of implicit argumentation. We annotated a large number of implicit arguments for this group of predicates with the goal of training models that generalize well to the testing data. In the following section, we describe the implicit argument annotation process and resulting data set.

3. Implicit Argument Annotation and Analysis

As shown in the previous section, the existence of implicit arguments has been recognized for quite some time. This type of information, however, was not formally annotated until Ruppenhofer et al. (2010) conducted their SemEval task on implicit argument identification. There are two reasons why we chose to create an independent data set for implicit arguments. The first reason is the aforementioned sparsity of the SemEval data set. The second reason is that the SemEval data set is not built on top of the Penn TreeBank, which is the gold-standard syntactic base for all work in this article. Working on top of the Penn TreeBank makes the annotations immediately compatible with PropBank, NomBank, and a host of other resources that also build on the TreeBank.

3.1 Data Annotation

3.1.1 Predicate Selection

Implicit arguments are a relatively new subject of annotation in the field. To effectively use our limited annotation resources and allow the observation of interesting behaviors, we decided to focus on a select group of nominal predicates. Predicates in this group were required to meet the following criteria:

  • 1. 

    A selected predicate must have an unambiguous role set. This criterion corresponds roughly to an unambiguous semantic sense and is motivated by the need to separate the implicit argument behavior of a predicate from its semantic meaning.

  • 2. 

    A selected predicate must be derived from a verb. This article focuses primarily on the event structure of texts. Nominal predicates derived from verbs denote events, but there are other, non-eventive predicates in NomBank (e.g., the partitive predicate indicated by the “%” symbol). This criterion also implies that the annotated predicates have correlates in PropBank with semantically compatible role sets.

  • 3. 

    A selected predicate should have a high frequency in the Penn TreeBank corpus. This criterion ensures that the evaluation results say as much as possible about the event structure of the underlying corpus. We calculated frequency with basic counting over morphologically normalized predicates (i.e., bids and bid are counted as the same predicate).

  • 4. 

    A selected predicate should express many implicit arguments. Of course, this can only be estimated ahead of time because no data exist to compute it. To estimate this value for a predicate p, we first calculated Np, the average number of roles expressed by p in NomBank. We then calculated Vp, the average number of roles expressed by the verb form of p in PropBank. We hypothesized that the difference VpNp gives an indication of the number of implicit arguments that might be present in the text for a nominal instance of p. The motivation for this hypothesis is as follows. Most verbs must be explicitly accompanied by specific arguments in order for the resulting sentence to be grammatical. The following sentences are ungrammatical if the parenthesized portion is left out:

  • (18) 

    *John loaned (the money to Mary).

  • (19) 

    *John invested (his money).

  • Examples (18) and (19) indicate that certain arguments must explicitly accompany loan and invest. In nominal form, these predicates can exist without such arguments and still be grammatical:

  • (20) 

    John’s loan was not repaid.

  • (21) 

    John’s investment was huge.

  • Note, however, that Examples (20) and (21) are not reasonable things to write unless the missing arguments were previously mentioned in the text. This is precisely the type of noun that should be targeted for implicit argument annotation. The value of VpNp thus quantifies the desired behavior.

Predicates were filtered according to criteria 1 and 2 and ranked according to the product of criteria 3 and 4. We then selected the top ten, which are shown in the first column of Table 1. The role sets (i.e., argument definitions) for these predicates can be found in the Appendix on page 790.

Table 1 

Annotation data analysis. Columns are defined as follows: (1) the annotated predicate, (2) the number of predicate instances that were annotated, (3) the average number of implicit arguments per predicate instance, (4) of all roles for all predicate instances, the percentage filled by NomBank arguments, (5) the average number of NomBank arguments per predicate instance, (6) the average number of PropBank arguments per instance of the verb form of the predicate, (7) of all roles for all predicate instances, the percentage filled by either NomBank or implicit arguments, (8) the average number of combined NomBank/implicit arguments per predicate instance. SD indicates the standard deviation with respect to an average.


Pre-annotation
Post-annotation

Role avg. (SD)

Pred.
# Pred.
# Imp./pred.
Role coverage (%)
Noun
Verb
Role coverage (%)
Noun role avg. (SD)
bid 88 1.4 26.9 0.8 (0.6) 2.2 (0.6) 73.9 2.2 (0.9) 
sale 184 1.0 24.2 1.2 (0.7) 2.0 (0.7) 44.0 2.2 (0.9) 
loan 84 1.0 22.1 1.1 (1.1) 2.5 (0.5) 41.7 2.1 (1.1) 
cost 101 0.9 26.2 1.0 (0.7) 2.3 (0.5) 47.5 1.9 (0.6) 
plan 100 0.8 30.8 1.2 (0.8) 1.8 (0.4) 50.0 2.0 (0.4) 
investor 160 0.7 35.0 1.1 (0.2) 2.0 (0.7) 57.5 1.7 (0.6) 
price 216 0.6 42.5 1.7 (0.5) 1.7 (0.5) 58.6 2.3 (0.6) 
loss 104 0.6 33.2 1.3 (0.9) 2.0 (0.6) 48.1 1.9 (0.7) 
investment 102 0.5 15.7 0.5 (0.7) 2.0 (0.7) 33.3 1.0 (1.0) 
fund 108 0.5 8.3 0.3 (0.7) 2.0 (0.3) 21.3 0.9 (1.2) 
Overall 1,247 0.8 28.0 1.1 (0.8) 2.0 (0.6) 47.8 1.9 (0.9) 
(1) (2) (3) (4) (5) (6) (7) (8) 

Pre-annotation
Post-annotation

Role avg. (SD)

Pred.
# Pred.
# Imp./pred.
Role coverage (%)
Noun
Verb
Role coverage (%)
Noun role avg. (SD)
bid 88 1.4 26.9 0.8 (0.6) 2.2 (0.6) 73.9 2.2 (0.9) 
sale 184 1.0 24.2 1.2 (0.7) 2.0 (0.7) 44.0 2.2 (0.9) 
loan 84 1.0 22.1 1.1 (1.1) 2.5 (0.5) 41.7 2.1 (1.1) 
cost 101 0.9 26.2 1.0 (0.7) 2.3 (0.5) 47.5 1.9 (0.6) 
plan 100 0.8 30.8 1.2 (0.8) 1.8 (0.4) 50.0 2.0 (0.4) 
investor 160 0.7 35.0 1.1 (0.2) 2.0 (0.7) 57.5 1.7 (0.6) 
price 216 0.6 42.5 1.7 (0.5) 1.7 (0.5) 58.6 2.3 (0.6) 
loss 104 0.6 33.2 1.3 (0.9) 2.0 (0.6) 48.1 1.9 (0.7) 
investment 102 0.5 15.7 0.5 (0.7) 2.0 (0.7) 33.3 1.0 (1.0) 
fund 108 0.5 8.3 0.3 (0.7) 2.0 (0.3) 21.3 0.9 (1.2) 
Overall 1,247 0.8 28.0 1.1 (0.8) 2.0 (0.6) 47.8 1.9 (0.9) 
(1) (2) (3) (4) (5) (6) (7) (8) 

3.1.2 Annotation Procedure

We annotated implicit arguments for instances of the ten selected nominal predicates. The annotation process proceeded document-by-document. For a document d, we annotated implicit arguments as follows:

  • 1. 

    Select from d all non-proper singular and non-proper plural nouns that are morphologically related to the ten predicates in Table 1.

  • 2. 

    By design, each selected noun has an unambiguous role set. Thus, given the arguments supplied for a noun by NomBank, one can consult the noun’s role set to determine which arguments are missing.5

  • 3. 

    For each missing argument position, search the current sentence and all preceding sentences for a suitable implicit argument. Annotate all suitable implicit arguments in this window.

  • 4. 

    When possible, match the textual bounds of an implicit argument to the textual bounds of an argument given by either PropBank or NomBank. This was done to maintain compatibility with these and other resources.

In the remainder of this article, we will use iargn to refer to an implicit argument position n. We will use argn to refer to an argument provided by PropBank or NomBank. We will use p to mark predicate instances. Example (22) provides a sample annotation for an instance of the investment predicate:
  • (22) 

    [iarg0 Participants] will be able to transfer [iarg1 money] to [iarg2 other investment funds]. The [p investment] choices are limited to [iarg2 a stock fund and a money-market fund].

NomBank does not associate this instance of investment with any arguments; one can easily identify the investor (iarg0), the thing invested (iarg1), and two mentions of the thing invested in (iarg2) within the surrounding discourse, however.

Of course, not all implicit argument decisions are as easy as those in Example (22). Consider the following contrived example:

  • (23) 

    People in other countries could potentially consume large amounts of [iarg0? Coke].

  • (24) 

    Because of this, there are [p plans] to expand [iarg0 the company’s] international presence.

Example (24) contains one mention of the iarg0 (the agentive planner). It might be tempting to also mark Coke in Example (23) as an additional iarg0; the only reasonable interpretation of Coke in 23 is as a consumable fluid, however. Fluids cannot plan things, so this annotation should not be performed. This is a case of sense ambiguity between Coke as a company and Coke as a drink. In all such cases, the annotator was asked to infer the proper sense before applying an implicit argument label.

Lastly, it should be noted that we placed no restrictions on embedded arguments. PropBank and NomBank do not allow argument extents to overlap. Traditional SRL systems such as the one created by Punyakanok, Roth, and Yih (2008) model this constraint explicitly to arrive at the final label assignment; as the following example shows, however, this constraint should not be applied to implicit arguments:

  • (25) 

    Currently, the rules force [iarg0 executives, directors and other corporate insiders] to report purchases and [p sales] [arg1 of [iarg0 their] companies’ shares] within about a month after the transaction.

Despite its embedded nature, the pronoun their in Example (25) is a perfectly reasonable implicit argument (the seller) for the marked predicate. Systems should be required to identify such arguments; thus, we included them in our annotations.

3.1.3 Inter-annotator Agreement

Implicit argument annotation is a difficult task because it combines the complexities of traditional SRL annotation with those of coreference annotation. To assess the reliability of the annotation process described previously, we compared our annotations to those provided by an undergraduate linguistics student who, after a brief training period, re-annotated a portion of the data set. For each missing argument position, the student was asked to identify the textually closest acceptable implicit argument within the current and preceding sentences. The argument position was left unfilled if no acceptable constituent could be found. For a missing argument position iargn, the student’s annotation agreed with our own if both identified the same implicit argument or both left iargn unfilled. The student annotated 480 of the 1,247 predicate instances shown in Table 1.

We computed Cohen’s chance-corrected kappa statistic for inter-annotator agreement (Cohen 1960), which is based on two quantities:
formula
The quantity 1 − pc indicates the probability of a chance disagreement. The quantity popc indicates the probability of agreement that cannot be accounted for by chance alone. Finally, Cohen defines κ as follows:
formula
Cohen’s kappa thus gives the probability that a chance-expected disagreement will not occur. When agreement is perfect, κ = 1. If the observed agreement is less than the expected chance agreement, then κ will be negative. As noted by Di Eugenio and Glass (2004), researchers have devised different scales to assess κ. Many NLP researchers use the scale created by Krippendorff (1980):
formula
Di Eugenio and Glass (2004) note, however, that this scale has not been rigorously defended, even by Krippendorff (1980) himself.
For the implicit argument annotation data, observed and chance agreement are defined as follows:
formula
where N is the total number of missing argument positions that need to be annotated, agree is equal to 1 if the two annotators agreed on iargn and 0 otherwise, PA(n) and PB(n) are the observed prior probabilities that annotators A and B assign the argument label n to a filler, and random_agree is equal to the probability that both annotators would select the same implicit argument for iargn when choosing randomly from the discourse. In Equation (1), terms to the right of + denote the probability that the two annotators agreed on iargn because they did not identify a filler for it.

Using these values for po and pc, Cohen’s kappa indicated an agreement of 64.3%. According to the scale of Krippendorff (1980), this value is borderline between low and moderate agreement. Possible causes for this low agreement include the brief training period for the linguistics student and the sheer complexity of the annotation task. If one considers only those argument positions for which both annotators actually located an implicit filler, Cohen’s kappa indicates an agreement of 93.1%. This shows that much of the disagreement concerned the question of whether a filler was present. Having agreed that a filler was present, the annotators consistently selected the same filler. Subsequently, we demonstrate this situation with actual data. First, we present our annotations for two sentences from the same document:

  • (26) 

    Shares of UAL, the parent of [iarg1 United Airlines], were extremely active all day Friday, reacting to news and rumors about the proposed [iarg2 $6.79 billion] buy-out of [iarg1 the airline] by an employee–management group.

  • (27) 

    And 10 minutes after the UAL trading halt came news that the UAL group couldn’t get financing for [arg0 its] [p bid].

In Example (27), the predicate is marked along with the explicit arg0 argument. Our task is to locate the implicit iarg1 (the entity bid for) and the implicit iarg2 (the amount of the bid). We were able to locate these entities in a previous sentence (Example (26)). Next, we present the second annotator’s (i.e., the student’s) annotations for the same two sentences:
  • (28) 

    Shares of UAL, the parent of [iarg1 United Airlines], were extremely active all day Friday, reacting to news and rumors about the proposed $6.79 billion buy-out of [iarg1 the airline] by an employee–management group.

  • (29) 

    And 10 minutes after the UAL trading halt came news that the UAL group couldn’t get financing for [arg0 its] [p bid].

As shown in Example (28), the second annotator agreed with our identification of the iarg1; the second annotator did not mark an implicit iarg2, however, despite the fact that it can be inferred. We believe this type of error can be addressed with additional training. The student’s annotations were only used to compute agreement. We performed all training and evaluation using randomized cross-validation over the annotations we created.

3.2 Annotation Analysis

We carried out this annotation process on the standard training (2–21), development (24), and testing (23) sections of the Penn TreeBank. Table 1 summarizes the results. In this section, we highlight key pieces of information found in this table.

3.2.1 Implicit Arguments are Frequent

Column (3) of Table 1 shows that most predicate instances are associated with at least one implicit argument. Implicit arguments vary across predicates, with bid exhibiting (on average) more than one implicit argument per instance versus the 0.5 implicit arguments per instance of the investment and fund predicates. It turned out that the latter two predicates have unique senses that preclude implicit argumentation (more on this in Section 6).

3.2.2 Implicit Arguments Create Fuller Event Descriptions

Role coverage for a predicate instance is equal to the number of filled roles divided by the number of roles in the predicate’s role set. Role coverage for the marked predicate in Example (22) is 0/3 for NomBank-only arguments and 3/3 when the annotated implicit arguments are also considered. Returning to Table 1, the fourth column gives role coverage percentages for NomBank-only arguments. The seventh column gives role coverage percentages when both NomBank arguments and the annotated implicit arguments are considered. Overall, the addition of implicit arguments created a 71% relative (20-point absolute) gain in role coverage across the 1,247 predicate instances that we annotated.

3.2.3 The VpNp Predicate Selection Metric Behaves as Desired

The predicate selection method used the VpNp metric to identify predicates whose instances are likely to take implicit arguments. Column (5) in Table 1 shows that (on average) nominal predicates have 1.1 arguments in NomBank; this compared to the 2.0 arguments per verbal form of the predicates in PropBank (compare columns (5) and (6)). We hypothesized that this difference might indicate the presence of approximately one implicit argument per predicate instance. This hypothesis is confirmed by comparing columns (6) and (8): When considering implicit arguments, many nominal predicates express approximately the same number of arguments on average as their verbal counterparts.

3.2.4 Most Implicit Arguments Are Nearby

In addition to these analyses, we examined the location of implicit arguments in the discourse. Figure 1 shows that approximately 56% of the implicit arguments in our data can be resolved within the sentence containing the predicate. Approximately 90% are found within the previous three sentences. The remaining implicit arguments require up to 4–6 sentences for resolution. These observations are important; they show that searching too far back in the discourse is likely to produce many false positives without a significant increase in recall. Section 6 discusses additional implications of this skewed distribution.

Figure 1

Location of implicit arguments. Of all implicitly filled argument positions, the y-axis indicates the percentage that are filled at least once within the number of sentences indicated by the x-axis (multiple fillers may exist for the same position).

Figure 1

Location of implicit arguments. Of all implicitly filled argument positions, the y-axis indicates the percentage that are filled at least once within the number of sentences indicated by the x-axis (multiple fillers may exist for the same position).

4. Implicit Argument Model

4.1 Model Formulation

Given a nominal predicate instance p with a missing argument position iargn, the task is to search the surrounding discourse for a constituent c that fills iargn. The implicit argument model conducts this search over all constituents that are marked with a core argument label (arg0, arg1, etc.) associated with a NomBank or PropBank predicate. Thus, the model assumes a pipeline organization in which a document is initially analyzed by traditional verbal and nominal SRL systems. The core arguments from this stage then become candidates for implicit argumentation. Adjunct arguments are excluded.

A candidate constituent c will often form a coreference chain with other constituents in the discourse. Consider the following abridged sentences, which are adjacent in their Penn TreeBank document:

  • (30) 

    [Mexico] desperately needs investment.

  • (31) 

    Conservative Japanese investors are put off by [Mexico’s] investment regulations.

  • (32) 

    Japan is the fourth largest investor in [c Mexico], with 5% of the total [p investments].

NomBank does not associate the labeled instance of investment with any arguments, but it is clear from the surrounding discourse that constituent c (referring to Mexico) is the thing being invested in (the iarg2). When determining whether c is the iarg2 of investment, one can draw evidence from other mentions in c’s coreference chain. Example (30) states that Mexico needs investment. Example (31) states that Mexico regulates investment. These propositions, which can be derived via traditional SRL analyses, should increase our confidence that c is the iarg2 of investment in Example (32).

Thus, the unit of classification for a candidate constituent c is the three-tuple 〈p, iargn, c′〉, where c′ is a coreference chain comprising c and its coreferent constituents.6 We defined a binary classification function Pr(+∣ 〈p, iargn, c′〉) that predicts the probability that the entity referred to by c fills the missing argument position iargn of predicate instance p. In the remainder of this article, we will refer to c as the primary filler, differentiating it from other mentions in the coreference chain c′. This distinction is necessary because our evaluation requires the model to select at most one filler (i.e., c) for each missing argument position. In the following section, we present the feature set used to represent each three-tuple within the classification function.

4.2 Model Features

Appendix Table B.1 lists all features used by the model described in the previous section. For convenience, Table 2 presents a high-level grouping of the features and the resources used to compute them. The broadest distinction to be made is whether a feature depends on elements of c′. Features in Group 3 do not, whereas all others do. The features in Group 3 characterize the predicate–argument position being filled (p and iargn), independently of the candidate filler. This group accounts for 43% of the features and 35% of those in the top 20.7 The remaining features depend on elements of c′ in some way. Group 1 features characterize the tuple using the SRL propositions contained in the text being evaluated. Group 2 features place the 〈p, iargn, c′〉 tuple into a manually constructed ontology and compute a value based on the structure of that ontology. Group 4 features compute statistics of the tuple within a large corpus of semantically analyzed text. Group 5 contains a single feature that captures the discourse structure properties of the tuple. Group 6 contains all other features, most of which capture the syntactic relationships between elements of c′ and p. In the following sections, we provide detailed examples of features from each group shown in Table 2.

Table 2 

Primary feature groups used by the model. The third column gives the number of features in the group, and the final column gives the number of features from the group that were ranked in the top 20 among all features.

Feature group
Resources used
#
Top 20
(1) Textual semantics PropBank, NomBank 13 
(2) Ontologies FrameNet, VerbNet, WordNet 
(3) Filler-independent Penn TreeBank 35 
(4) Corpus statistics Gigaword, Verbal SRL, Nominal SRL 
(5) Textual discourse Penn Discourse Bank 
(6) Other Penn TreeBank 15 
Feature group
Resources used
#
Top 20
(1) Textual semantics PropBank, NomBank 13 
(2) Ontologies FrameNet, VerbNet, WordNet 
(3) Filler-independent Penn TreeBank 35 
(4) Corpus statistics Gigaword, Verbal SRL, Nominal SRL 
(5) Textual discourse Penn Discourse Bank 
(6) Other Penn TreeBank 15 

4.2.1 Group 1: Features Derived from the Semantic Content of the Text

Feature 1 was often selected first by the feature selection algorithm. This feature captures the semantic properties of the candidate filler c′ and the argument position being filled. Consider the following Penn TreeBank sentences:

  • (33) 

    [arg0 The two companies] [p produce] [arg1 market pulp, containerboard and white paper]. The goods could be manufactured closer to customers, saving [p shipping] costs.

Here we are trying to fill the iarg0 of shipping. Let c′ contain a single mention, The two companies, which is the arg0 of produce. Feature 1 takes a value of producearg0shiparg0. This value, which is derived from the text itself, asserts that producers are also shippers. To reduce data sparsity, we generalized the predicates to their WordNet synset IDs (creating Feature 4). We also generalized the predicates and arguments to their VerbNet thematic roles using SemLink (creating Feature 23). Although the generalized features rely on ontologies, they do so in a trivial way that does not take advantage of the detailed structure of the ontologies. Such structure is used by features in the next group.

4.2.2 Group 2: Features Derived from Manually Constructed Ontologies

Feature 9 captures the semantic relationship between predicate–argument positions by examining paths between frame elements in FrameNet. SemLink8 maps PropBank argument positions to their FrameNet frame elements. For example, the arg1 position of sell maps to the Goods frame element of the Sell frame. NomBank argument positions (e.g., arg1 of sale) can be mapped to FrameNet by first converting the nominal predicate to its verb form. By mapping predicate–argument structures into FrameNet, one can take advantage of the rich network of frame–frame relations provided by the resource.

The value of Feature 9 has the following general form:

  • (34) 

This path describes how the frame elements at either end are related. For example, consider the frame element path between the arg1 of sell and the arg1 of buy, both of which denote the goods being transferred:
  • (35) 

This path can be paraphrased as follows: things that are sold (Sell.Goods) are part of a more general give scenario (Give.Theme) that can also be viewed as a get scenario (Get.Theme) in which the buyer receives something (Buy.Goods). This complex world knowledge is represented compactly using the relationships defined in FrameNet. In our experiments, we searched all possible frame element paths of length five or less that use the following relationships:
  • • 

    Causative–of

  • • 

    Inchoative–of

  • • 

    Inherits

  • • 

    Precedes

  • • 

    Subframe–of

Feature 9 is helpful in situations such as the following (contrived):

  • (36) 

    Consumers bought many [c cars] this year at reduced prices.

  • (37) 

    [p Sales] are expected to drop when the discounts are eliminated.

In Example (37) we are looking for the iarg1 (thing sold) of sale. The path shown in Example (35) indicates quite clearly that the candidate cars from Example (36), being the entity purchased, is a suitable filler for this position. Lastly, note that the value for Feature 9 is the actual path instead of a numeric value. When c′ contains multiple coreferential elements, this feature can be instantiated using multiple values (i.e., paths). Ultimately, these instantiations are binarized into the LibLinear input format, so the existence of multiple feature values does not pose a problem.

Feature 59 is similar to Feature 9 (the frame element path) except that it captures the distance between predicate–argument positions within the VerbNet hierarchy. Consider the following VerbNet classes:

  • 13.2 

    lose, refer, relinquish, remit, resign, restore, gift, hand out, pass out, shell out

  • 13.5.1.1 

    earn, fetch, cash, gain, get, save, score, secure, steal

The path from earn to lose in the VerbNet hierarchy is as follows:
  • (38) 

    13.5.1.1 ↑13.5.1 ↑13.5 ↑13 ↓13.2

The path in Example (38) is four links long. Intuitively, earn and lose are related to each other—they describe two possible outcomes of a financial transaction. The VerbNet path quantifies this intuition, with shorter paths indicating closer relationships. This information can be used to identify implicit arguments in situations such as the following from the Penn TreeBank (abridged):
  • (39) 

    [c Monsanto Co.] is expected to continue reporting higher [p earnings].

  • (40) 

    The St. Louis-based company is expected to report that [p losses] are narrowing.

In Example (40) we are looking for the iarg0 (i.e., entity losing something) for the loss predicate. According to SemLink, this argument position maps to the 13.2.Agent role in VerbNet. In Example (39), we find the candidate implicit argument Monsanto Co., which is the arg0 to the earning predicate in that sentence. This argument position maps to the 13.5.1.1.Agent role in VerbNet. These two VerbNet roles are related according to the VerbNet path in Example (38), producing a value for Feature 59 of four. This relatively small value supports an inference of Monsanto Co. as the iarg0 for loss. It is important to note that a VerbNet path only exists when the thematic roles are identical. For example, a VerbNet path would not exist between 13.5.1.1.Theme and 13.2.Agent because the roles are not compatible. Lastly, recall that c′ might contain multiple coreferential elements. In such a situation, the minimum path length is selected as the value for this feature.

4.2.3 Group 3: Filler-independent Features

Many of the features used by the model do not depend on elements of c′. These features are usually specific to a particular predicate. Consider the following example:

  • (41) 

    Statistics Canada reported that its [arg1 industrial–product] [p price] index dropped 2% in September.

The “[p price] index” collocation is rarely associated with an arg0 in NomBank or with an iarg0 in the annotated data (both argument positions denote the seller). Feature 25 accounts for this type of behavior by encoding the syntactic head of p's right sibling. The value of Feature 25 for Example 41 is price:index. Contrast this with the following:
  • (42) 

    [iarg0 The company] is trying to prevent further [p price] drops.

The value of Feature 25 for Example (42) is price:drop. This feature captures an important distinction between the two uses of price: the former cannot easily take an iarg0, whereas the latter can. Many other features in Table B.1 depend only on the predicate and have values that take the form predicate:feature_value.

4.2.4 Group 4: Features Derived from Corpus Statistics

Feature 13 is inspired by the work of Chambers and Jurafsky (2008), who investigated unsupervised learning of narrative event sequences using pointwise mutual information (PMI) between syntactic positions. We extended this PMI score to semantic arguments instead of syntactic dependencies. Thus, the value for this feature is computed as follows:
formula
We computed Equation (2) by applying verbal SRL (Punyakanok, Roth, and Yih 2008), nominal SRL (Gerber, Chai, and Meyers 2009), and coreference identification (OpenNLP) to the Gigaword corpus (Graff 2003); because these tools are not fast enough to process all 1,000,000 documents in the corpus, however, we selected subsets of the corpus for each p1/p2 combination observed in our implicit argument data. We first indexed the Gigaword corpus using the Lucene search engine.9 We then queried this index using the simple boolean query “p1 AND p2,” which retrieved documents relevant to the predicates considered in Equation (2). Assuming the resulting data have N coreferential pairs of arguments, the numerator in Equation (2) is defined as follows:
formula
In Equation (3), #coref returns the number of times the given argument positions are found to be coreferential. In order to penalize low-frequency observations with artificially high scores, we used the simple discounting method described by Pantel and Ravichandran (2004) resulting in the following modification of Equation (3):
formula
Thus, if two argument positions are rarely observed as coreferent, the value will be small, reducing the PMI score. The denominator in Equation (2) is computed with a similar discount factor:
formula
Thus, if either of the argument positions is rarely observed as coreferent with other argument positions, the value will be small, making the denominator of Equation (2) large, reducing the PMI score. In general, the discount factors reduce the PMI score for argument positions that are not frequent in the corpus.

We refer to Equation (2) as a targeted PMI score because it relies on data that have been chosen specifically for the calculation at hand. Table 3 shows a sample of targeted PMI scores between the arg1 of loss and other argument positions. There are two things to note about this data: First, the argument positions listed are all naturally related to the arg1 of loss. Second, the discount factor changes the final ranking by moving the less frequent recoup predicate from a raw rank of 1 to a discounted rank of 3, preferring instead the more common win predicate.

Table 3 

Targeted PMI scores between the arg1 of loss and other argument positions. The second column gives the number of times that the argument position in the row is found to be coreferent with the arg1 of the loss predicate. A higher value in this column results in a lower discount factor. See Equation (4) for the discount factor.

Argument position
#coref with loss.arg1
Raw PMI score
Discounted PMI score
win.arg1 37 5.68 5.52 
gain.arg1 10 5.13 4.64 
recoup.arg1 6.99 4.27 
steal.arg1 5.18 4.09 
possess.arg1 5.10 3.77 
Argument position
#coref with loss.arg1
Raw PMI score
Discounted PMI score
win.arg1 37 5.68 5.52 
gain.arg1 10 5.13 4.64 
recoup.arg1 6.99 4.27 
steal.arg1 5.18 4.09 
possess.arg1 5.10 3.77 

The information in Table 3 is useful in situations such as the following (contrived):

  • (43) 

    Mary won [c the tennis match].

  • (44) 

    [arg0 John’s] [p loss] was not surprising.

In Example (44) we are looking for the iarg1 of loss. The information in Table 3 strongly suggests that the marked candidate c, being the arg1 of win, would be a suitable filler for this position. Lastly, note that if c were to form a coreference chain with other constituents, it would be possible to calculate multiple PMI scores. In such cases, the targeted PMI feature uses the maximum of all scores.

Feature 27 captures the selectional preference of a predicate p for the elements in c′ with respect to argument position iargn. In general, selectional preference scores denote the strength of attraction for a predicate–argument position to a particular word or class of words. To calculate the value for this feature, we used the information–theoretic model proposed by Resnik (1996), which is defined as follows:
formula
In Equation (6), Pref calculates the preference for a WordNet synset s in the given predicate–argument position. Prior and posterior probabilities for s were calculated by examining the arguments present in the Penn TreeBank combined with 20,000 documents randomly selected from the Gigaword corpus. PropBank and NomBank supplied arguments for the Penn TreeBank, and we used the aforementioned verbal and nominal SRL systems to extract arguments from Gigaword. The head word for each argument was mapped to its WordNet synsets, and counts for these synsets were updated as suggested by Resnik (1996). Note that a synset s that is not observed in the training data will receive a score of zero because Pr(s|p,argn) will be zero.
Equation (6) computes the preference of a predicate–argument position for a synset; a single word can map to multiple synsets if its sense is ambiguous, however. Given a word w and its synsets s1, s2, …, sn, the preference of a predicate–argument position for w is defined as follows:
formula
That is, the preference for a word is computed as the average preference across all possible synsets. The final value for Feature 27 is computed using the word-based preference score defined in Equation (7). Given a candidate implicit argument c′ comprising the primary filler c and its coreferent mentions, the following value is obtained:
formula
In Equation (8), each f is the syntactic head of a constituent from c′. The value of Equation (8) is in ( − ∞ , + ∞ ), with larger values indicating higher preference for c as the implicit filler of position iargn.
Feature 33 implements the suggestion of Burchardt, Frank, and Pinkal (2005) that implicit arguments might be identified using observed coreference patterns in a large corpus of text. Our implementation of this feature uses the same data used for the previous feature: arguments extracted from the Penn TreeBank and 20,000 documents randomly selected from Gigaword. Additionally, we identified coreferent arguments in this corpus using OpenNLP. Using this information, we calculated the probability of coreference between any two argument positions. As with Feature 13, we used discounting to penalize low–frequency observations, producing an estimate of coreference probability as follows:
formula

In Equation (9), Pcoref should be read as “the probability that 〈p1,argi〉 is coreferential with 〈p2,argj〉 given 〈p1,argi〉 is coreferential with something.” For example, we observed that the arg1 for predicate reassess (the entity reassessed) is coreferential with six other constituents in the corpus. Table 4 lists the argument positions with which this argument is coreferential along with the raw and discounted probabilities. The discounted probabilities can help identify the implicit argument in the following contrived examples:

  • (45) 

    Senators must rethink [c their strategy for the upcoming election].

  • (46) 

    The [p reassessment] must begin soon.

In Example (46) we are looking for the iarg1 of reassess. Table 4 tells us that the marked candidate (an arg1 to rethink) is likely to fill this missing argument position. When c forms a coreference chain with other constituents, this feature uses the minimum coreference probability between the implicit argument position and elements in the chain.

Table 4 

Coreference probabilities between reassess.arg1 and other argument positions. See Equation (9) for details on the discount factor.

Argument
Raw coreference probability
Discounted coreference probability
rethink.arg1 3/6 = 0.5 0.32 
define.arg1 2/6 = 0.33 0.19 
redefine.arg1 1/6 = 0.17 0.07 
Argument
Raw coreference probability
Discounted coreference probability
rethink.arg1 3/6 = 0.5 0.32 
define.arg1 2/6 = 0.33 0.19 
redefine.arg1 1/6 = 0.17 0.07 

4.2.5 Group 5: Features Derived from the Discourse Structure of the Text

Feature 67 identifies the discourse relation (if any) that holds between the candidate constituent c and the filled predicate p. Consider the following example:

  • (47) 

    [iarg0 SFE Technologies] reported a net loss of $889,000 on sales of $23.4 million.

  • (48) 

    That compared with an operating [p loss] of [arg1 $1.9 million] on sales of $27.4 million in the year–earlier period.

In this case, a comparison discourse relation (signaled by the underlined text) holds between the first and second sentence. The coherence provided by this relation encourages an inference that identifies the marked iarg0 (the loser). The value for this feature is the name of the discourse relation (e.g., comparison) whose two discourse units cover the candidate (iarg0 above) and filled predicate (p above). Throughout our investigation, we used gold-standard discourse relations provided by the Penn Discourse TreeBank (Prasad et al. 2008).

4.2.6 Group 6: Other Features

A few other features that were prominent according to our feature selection process are not contained in the groups described thus for. Feature 2 encodes the sentence distance from c (the primary filler) to the predicate for which we are filling the implicit argument position. The prominent position of this feature agrees with our previous observation that most implicit arguments can be resolved within a few sentences of the predicate (see Figure 1 on p. 16). Feature 3 is another simple yet highly ranked feature. This feature concatenates the head of an element of c′ with p and iargn. For example, in sentences (45) and (46), this feature would have a value of strategyreassessarg1, asserting that strategies are reassessed. Feature 5 generalizes this feature by replacing the head word with its WordNet synset.

4.2.7 Comparison with Features for Traditional SRL

The features described thus far are quite different from those used in previous work to identify arguments in the traditional nominal SRL setting (see the work of Gerber, Chai, and Meyers 2009). The most important feature used in traditional SRL—the syntactic parse tree path—is notably absent. This difference is due to the fact that syntactic information, although present, does not play a central role in the implicit argument model. The most important features are those that capture semantic properties of the implicit predicate–argument position and the candidate filler for that position.

4.3 Post-processing for Final Output Selection

Without loss of generality, assume there exists a predicate instance p with two missing argument positions iarg0 and iarg1. Also assume that there are three candidate fillers c1, c2, and c3 within the candidate window. The discriminative model will calculate the probability that each candidate fills each missing argument position. Graphically:

graphic

There exist two constraints on possible assignments of candidates to positions. First, a candidate may not be assigned to more than one missing argument position. To enforce this constraint, only the top-scoring cell in each row is retained, leading to the following:

graphic

Second, a missing argument position can only be filled by a single candidate. To enforce this constraint, only the top-scoring cell in each column is retained, leading to the following:

graphic

Having satisfied these constraints, a threshold t is imposed on the remaining cell probabilities.10 Cells with probabilities less than t are cleared. Assuming that t = 0.42, the final assignment would be as follows:

graphic

In this case, c3 fills iarg0 with probability 0.6 and iarg1 remains unfilled. The latter outcome is desirable because not all argument positions have fillers that are present in the discourse.

5. Evaluation

5.1 Data

All evaluations in this study were performed using a randomized cross-validation configuration. The 1,247 predicate instances were annotated document by document. In order to remove any confounding factors caused by specific documents, we first randomized the annotated predicate instances. Following this, we split the predicate instances evenly into ten folds and used each fold as testing data for a model trained on the instances outside the fold. This evaluation set-up is an improvement versus the one we previously reported (Gerber and Chai 2010), in which fixed partitions were used for training, development, and testing.

During training, the system was provided with annotated predicate instances. The system identified missing argument positions and generated a set of candidates for each such position. A candidate three-tuple 〈p, iargn, c′〉 was given a positive label if the candidate implicit argument c (the primary filler) was annotated as filling the missing argument position; otherwise, the candidate three-tuple was given a negative label. During testing, the system was presented with each predicate instance and was required to identify all implicit arguments for the predicate.

Throughout the evaluation process we assumed the existence of gold-standard PropBank and NomBank information in all documents. This factored out errors from traditional SRL and affected the following stages of system operation:

  • • 

    Missing argument identification. The system was required to figure out which argument positions were missing. Each of the ten predicates was associated with an unambiguous role set, so determining the missing argument positions amounted to comparing the existing local arguments with the argument positions listed in the predicate’s role set. Because gold-standard local NomBank arguments were used, this stage produced no errors.

  • • 

    Candidate generation. As mentioned in Section 4.1, the set of candidates for a missing argument position contains constituents labeled with a core (e.g., arg0) PropBank or NomBank argument label. We used gold-standard PropBank and NomBank arguments; it is not the case that all annotated implicit arguments are given a core argument label by PropBank or NomBank, however. Thus, despite the gold-standard argument labels, this stage produced errors in which the system failed to generate a true-positive candidate for an implicit argument position. Approximately 96% of implicit argument positions are filled by gold-standard PropBank or NomBank arguments.

  • • 

    Feature extraction. Many of the features described in Section 4.2 rely on underlying PropBank and NomBank argument labels. For example, the top-ranked Feature 1 relates the argument position of the candidate to the missing argument position. In our experiments, values for this feature contained no errors because gold-standard PropBank and NomBank labels were used. Note, however, that many features were derived from the output of an automatic SRL process that occasionally produced errors (e.g., Feature 13, which used PMI scores between automatically identified arguments). These errors were present in both the training and evaluation stages.

We also assumed the existence of gold-standard syntactic structure when possible. This was done in order to focus our investigation on the semantic nature of implicit arguments.

5.2 Scoring Metrics

We evaluated system performance using the methodology proposed by Ruppenhofer et al. (2010). For each missing argument position of a predicate instance, the system was required to either (1) identify a single constituent that fills the missing argument position or (2) make no prediction and leave the missing argument position unfilled. To give partial credit for inexact argument bounds, we scored predictions using the Dice coefficient, which is defined as follows:
formula
Predicted contains the tokens that the model believes fill the implicit argument position. True is the set of tokens from a single annotated constituent that fills the missing argument position. The model’s prediction receives a score equal to the maximum Dice overlap across any one of the annotated fillers (AF):
formula
Precision is equal to the summed prediction scores divided by the number of argument positions filled by the model. Recall is equal to the summed prediction scores divided by the number of argument positions filled in the annotated data. Predictions not covering the head of a true filler were assigned a score of zero.11 For example, consider the following true and predicted labelings:
  • (49) 

    True labeling: [iarg0 Participants] will be able to transfer [iarg1 money] to [iarg2 other investment funds]. The [p investment] choices are limited to [iarg2 a stock fund and a money-market fund].

  • (50) 

    Predicted labeling: Participants will be able to transfer [iarg1 money] to other [iarg2 investment funds]. The [p investment] choices are limited to a stock fund and a money-market fund.

In the ground-truth (49) there are three implicit argument positions to fill. The hypothetical system has made predictions for two of the positions. The prediction scores are:
formula
Precision, recall, and F1 for the example predicate are calculated as follows:
formula
We calculated the F1 score for the entire testing fold by aggregating the counts used in the above precision and recall calculations. Similarly, we aggregated the counts across all folds to arrive at a single F1 score for the evaluated system.

We used a bootstrap resampling technique similar to those developed by Efron and Tibshirani (1993) to test the significance of the performance difference between various systems. Given a test pool comprising M missing argument positions iargn along with the predictions by systems A and B for each iargn, we calculated the exact p-value of the performance difference as follows:

  • 1. 

    Create r random resamples from M with replacement.

  • 2. 

    For each resample Ri, compute the system performance difference dRi = ARiBRi and store dRi in D.

  • 3. 

    Find the largest symmetric interval [min,max] around the mean of D that does not include zero.

  • 4. 

    The exact p-value equals the percentage of elements in D that are not in [min,max].

Experiments have shown that this simple approach provides accurate estimates of significance while making minimal assumptions about the underlying data distribution (Efron and Tibshirani 1993). Similar randomization tests have been used to evaluate information extraction systems (Chinchor, Lewis, and Hirschmant 1993).

5.3 LibLinear Model Configuration

Given a testing fold Ftest and a training fold Ftrain, we performed floating forward feature subset selection using only the information contained in Ftrain. We used an algorithm similar to the one described by Pudil, Novovicova, and Kittler (1994). As part of the feature selection process, we conducted a grid search for the best c and w LibLinear parameters, which govern the per-class cost of mislabeling instances from a particular class (Fan et al. 2008). Setting per-class costs helps counter the effects of class size imbalance, which is severe even when selecting candidates from the current and previous few sentences (most candidates are negative). We ran the feature selection and grid search processes independently for each Ftrain. As a result, the feature set and model parameters are slightly different for each fold.12 For all folds, we used LibLinear’s logistic regression solver and a candidate selection window of two sentences prior. As shown in Figure 1, this window imposes a recall upper bound of approximately 85%. The post-processing prediction threshold t was learned using a brute-force search that maximized the system’s performance over the data in Ftrain.

5.4 Baseline and Oracle Models

We compared the supervised model with the simple baseline heuristic defined below:

Fill iargn for predicate instance p with the nearest constituent in the two-sentence candidate window that fills argn for a different instance of p, where all nominal predicates are normalized to their verbal forms.

The normalization allows, for example, an existing arg0 for the verb invested to fill an iarg0 for the noun investment. This heuristic outperformed a more complicated heuristic that relied on the PMI score described in Section 4.2. We also evaluated an oracle model that made gold-standard predictions for candidates within the two-sentence prediction window.

5.5 Results

Table 5 presents the evaluation results for implicit argument identification. Overall, the discriminative model increased F1 performance by 21.4 percentage points (74.1%) compared to the baseline (p < 0.0001). Predicates with the highest number of implicit arguments (sale and price) showed F1 increases of 13.7 and 17.5 percentage points, respectively (p < 0.001 for both differences). As expected, oracle precision is 100% for all predictions, and the F1 difference between the discriminative and oracle systems is significant at p < 0.0001 for all test sets. See the Appendix for a per-fold breakdown of results and a listing of features and model parameters used for each fold.

Table 5 

Overall evaluation results for implicit argument identification. The second column gives the number of ground–truth implicitly filled argument positions for the predicate instances. P, R, and F1 indicate precision, recall, and F–measure (β = 1), respectively. pexact is the bootstrapped exact p-value of the F1 difference between two systems, where the systems are (B)aseline, (D)iscriminative, and (O)racle.


Baseline
Discriminative

Oracle


# Imp. args.
P
R
F1
P
R
F1
pexact(B,D)
P
R
F1
pexact(D,O)
sale 181 57.0 27.7 37.3 59.2 44.8 51.0 0.0003 100.0 72.4 84.0 ≪0.0001 
price 138 67.1 23.3 34.6 56.0 48.7 52.1 ≪0.0001 100.0 78.3 87.8 ≪0.0001 
bid 124 66.7 14.5 23.8 60.0 36.3 45.2 ≪0.0001 100.0 60.5 75.4 ≪0.0001 
investor 108 30.0 2.8 5.1 46.7 39.8 43.0 ≪0.0001 100.0 84.3 91.5 ≪0.0001 
cost 86 60.0 10.5 17.8 62.5 50.9 56.1 ≪0.0001 100.0 86.0 92.5 ≪0.0001 
loan 82 63.0 20.7 31.2 67.2 50.0 57.3 ≪0.0001 100.0 89.0 94.2 ≪0.0001 
plan 77 72.7 20.8 32.3 59.6 44.1 50.7 0.0032 100.0 87.0 93.1 ≪0.0001 
loss 62 78.8 41.9 54.7 72.5 59.7 65.5 0.0331 100.0 88.7 94.0 ≪0.0001 
fund 56 66.7 10.7 18.5 80.0 35.7 49.4 ≪0.0001 100.0 66.1 79.6 ≪0.0001 
investment 52 28.9 10.6 15.5 32.9 34.2 33.6 0.0043 100.0 80.8 89.4 ≪0.0001 
Overall 966 61.4 18.9 28.9 57.9 44.5 50.3 ≪0.0001 100.0 78.0 87.6 ≪0.0001 

Baseline
Discriminative

Oracle


# Imp. args.
P
R
F1
P
R
F1
pexact(B,D)
P
R
F1
pexact(D,O)
sale 181 57.0 27.7 37.3 59.2 44.8 51.0 0.0003 100.0 72.4 84.0 ≪0.0001 
price 138 67.1 23.3 34.6 56.0 48.7 52.1 ≪0.0001 100.0 78.3 87.8 ≪0.0001 
bid 124 66.7 14.5 23.8 60.0 36.3 45.2 ≪0.0001 100.0 60.5 75.4 ≪0.0001 
investor 108 30.0 2.8 5.1 46.7 39.8 43.0 ≪0.0001 100.0 84.3 91.5 ≪0.0001 
cost 86 60.0 10.5 17.8 62.5 50.9 56.1 ≪0.0001 100.0 86.0 92.5 ≪0.0001 
loan 82 63.0 20.7 31.2 67.2 50.0 57.3 ≪0.0001 100.0 89.0 94.2 ≪0.0001 
plan 77 72.7 20.8 32.3 59.6 44.1 50.7 0.0032 100.0 87.0 93.1 ≪0.0001 
loss 62 78.8 41.9 54.7 72.5 59.7 65.5 0.0331 100.0 88.7 94.0 ≪0.0001 
fund 56 66.7 10.7 18.5 80.0 35.7 49.4 ≪0.0001 100.0 66.1 79.6 ≪0.0001 
investment 52 28.9 10.6 15.5 32.9 34.2 33.6 0.0043 100.0 80.8 89.4 ≪0.0001 
Overall 966 61.4 18.9 28.9 57.9 44.5 50.3 ≪0.0001 100.0 78.0 87.6 ≪0.0001 

We also measured human performance on this task by running the undergraduate assistant’s annotations against a small portion of the evaluation data comprising 275 filled implicit arguments. The assistant achieved an overall F1 score of 56.0% using the same two-sentence candidate window used by the baseline, discriminative, and oracle models. Using an infinite candidate window, the assistant increased F1 performance to 64.2%. Although these results provide a general idea about the performance upper bound, they are not directly comparable to the cross-validated results shown in Table 5 because the assistant did not annotate the entire data set.

6. Discussion

6.1 Training Set Size

As described in Section 3.1, implicit argument annotation is an expensive process. Thus, it is important to understand whether additional annotation would benefit the ten predicates considered. In order to estimate the potential benefits, we measured the effect of training set size on system performance. We retrained the discriminative model for each evaluation fold using incrementally larger subsets of the complete training set for the fold. Figure 2 shows the results, which indicate minimal gains beyond 80% of the training set. Based on these results, we feel that future work should emphasize feature and model development over training data expansion, as gains appear to trail off significantly.

Figure 2

Effect of training set size on performance of discriminative model. The x-axis indicates the percentage of training data used, and the y-axis indicates the overall F1 score that results.

Figure 2

Effect of training set size on performance of discriminative model. The x-axis indicates the percentage of training data used, and the y-axis indicates the overall F1 score that results.

6.2 Feature Assessment

Previously (Gerber and Chai 2010), we assessed the importance of various implicit argument feature groups by conducting feature ablation tests. In each test, the discriminative model was retrained and reevaluated without a particular group of features. We summarize the findings of this study in this section.

6.2.1 Semantic Roles are Essential

We observed statistically significant losses when excluding features that relate the semantic roles of elements in c′ to the semantic role of the missing argument position. For example, Feature 1 appears as the top-ranked feature in eight out of ten fold evaluations (see Appendix Table C.1). This feature is formed by concatenating the filling predicate–argument position with the filled predicate–argument position, producing values such as invest.arg0-lose.arg0. This value indicates that the entity performing the investing is also the entity losing something. This type of commonsense knowledge is essential to the task of implicit argument identification.

6.2.2 Other Information is Important

Our 2010 study also found that semantic roles are only one part of the solution. Using semantic roles in isolation also produced statistically significant losses. This indicates that other features contribute useful information to the task.

6.2.3 Discourse Structure Is not Essential

We also tested the effect of removing discourse relations (Feature 67) from the model. Discourse structure has received a significant amount of attention in NLP; it remains a very challenging problem, however, with state-of-the-art systems attaining F1 scores in the mid-40% range (Sagae 2009). Our 2010 work as well as the updated work presented in this article used gold-standard discourse relations from the Penn Discourse TreeBank. As shown by Sagae (2009), these relations are difficult to extract in a practical setting. In our 2010 work, we showed that removing discourse relations from the model did not have a statistically significant effect on performance. Thus, this information should be removed in practical applications of the model, at least until better uses for it can be identified.

6.2.4 Relative Feature Importance

We extended earlier findings by assessing the relative importance of the features. We aggregated the feature rank information given in Appendix Table C.1. For each evaluation fold, each feature received a point value equal to its reciprocal rank within the feature list. Thus, a feature appearing at rank 5 for a fold would receive = 0.2 points for that fold. We totaled these points across all folds, arriving at the values shown in the final column of Appendix Table B.1. The scores confirm the earlier findings. The highest scoring feature relates the semantic roles of the candidate argument to the missing argument position. Non-semantic information such as the sentence distance (Feature 2) also plays a key role. Discourse structure is consistently ranked near the bottom of the list (Feature 67).

6.3 Error Analysis

Table 6 lists the errors made by the system and their frequencies. As shown, the single most common error (type 1) occurred when a true filler was classified but an incorrect filler had a higher score. This occurred in approximately 31% of the error cases. Often, though, the system did not classify a true implicit argument because such a candidate was not generated. Without such a candidate, the system stood no chance of making a correct prediction. Errors 3 and 5 combined (also 31%) describe this behavior. Type 3 errors resulted when implicit arguments were not core (i.e., argn) arguments to other predicates. To reduce class imbalance, the system only used core arguments as candidates; this came at the expense of increased type 3 errors, however. In many cases, the true implicit argument filled a non-core (i.e., adjunct) role within PropBank or NomBank.

Table 6 

Implicit argument error analysis. The second column indicates the type of error that was made and the third column gives the percentage of all errors that fall into each type.

#
Description
%
A true filler was classified but an incorrect filler scored higher 30.6 
A true filler did not exist but a prediction was made 22.4 
A true filler existed within the window but was not a candidate 21.1 
A true filler scored highest but below the threshold 15.9 
A true filler existed but not within the window 10.0 
#
Description
%
A true filler was classified but an incorrect filler scored higher 30.6 
A true filler did not exist but a prediction was made 22.4 
A true filler existed within the window but was not a candidate 21.1 
A true filler scored highest but below the threshold 15.9 
A true filler existed but not within the window 10.0 

Type 5 errors resulted when the true implicit arguments for a predicate were outside the candidate window. Oracle recall (see Table 5) indicates the nominals that suffered most from windowing errors. For example, the sale predicate was associated with the highest number of true implicit arguments, but only 72% of those could be resolved within the two-sentence candidate window. Empirically, we found that extending the candidate window uniformly for all predicates did not increase F1 performance because additional false positives were identified. The oracle results suggest that predicate-specific window settings might offer some advantage for predicates such as fund and bid, which take arguments at longer ranges.

Error types 2 and 4 are directly related to the prediction confidence threshold t. The former would be reduced by increasing t and thus filtering out bad predictions. The latter would be reduced by lowering t and allowing more true fillers into the final output. It is unclear whether either of these actions would increase overall performance, however.

6.4 The Investment and Fund Predicates

In Section 4.2, we discussed the price predicate, which frequently occurs in the “[p price] index” collocation. We observed that this collocation is rarely associated with either an overt arg0 or an implicit iarg0. Similar observations can be made for the investment and fund predicates. Although these two predicates are frequent, they are rarely associated with implicit arguments: investment takes only 52 implicit arguments and fund takes only 56 implicit arguments (see Table 5). This behavior is due in large part to collocations such as “[p investment] banker,” “stock [p fund],” and “mutual [p fund],” which use predicate senses that are not eventive and take no arguments. Such collocations also violate the assumption that differences between the PropBank and NomBank argument structure for a predicate are indicative of implicit arguments (see Section 3.1 for this assumption).

Despite their lack of implicit arguments, it is important to account for predicates such as investment and fund because the incorrect prediction of implicit arguments for them can lower precision. This is precisely what happened for the investment predicate (P = 33%). The model incorrectly identified many implicit arguments for instances such as “[p investment] banker” and “[p investment] professional,” which take no arguments. The right context of investment should help the model avoid this type of error; however in many cases this was not enough evidence to prevent a false positive prediction. It might be helpful to distinguish eventive nominals from non-eventive ones, given the observation that some non-eventive nominals rarely take arguments. Additional investigation is needed to address this type of error.

6.5 Improvements versus the Baseline

The baseline heuristic covers the simple case where identical predicates share arguments in the same position. Because the discriminative model also uses this information (see Feature 8), it is interesting to examine cases where the baseline heuristic failed but the discriminative model succeeded. Such cases represent more difficult inferences. Consider the following sentence:

  • (51) 

    Mr. Rogers recommends that [p investors] sell [iarg2 takeover–related stock].

Neither NomBank nor the baseline heuristic associate the marked predicate in Example (51) with any arguments; the feature-based model was able to correctly identify the marked iarg2 as the entity being invested in, however. This inference relied on a number of features that connect the invest event to the sell event (e.g., Features 1, 4, and 76). These features captured a tendency of investors to sell the things they have invested in.

We conclude our discussion with an example of a complex extra-sentential implicit argument. Consider the following adjacent sentences:

  • (52) 

    [arg0 Olivetti] [p exported] $25 million in “embargoed, state-of-the-art, flexible manufacturing systems to the Soviet aviation industry.”

  • (53) 

    [arg0 Olivetti] reportedly began [p shipping] these tools in 1984.

  • (54) 

    [iarg0 Olivetti] has denied that it violated the rules, asserting that the shipments were properly licensed.

  • (55) 

    However, the legality of these [p sales] is still an open question.

In Example (55), we are looking for the iarg0 of sale. As shown, the discriminative model was able to correctly identify Olivetti from Example (54) as the implied filler of this argument position. The inference involved two key steps. First, the model identified coreferent mentions of Olivetti in Examples (52) and (53). In these sentences, Olivetti participates in the marked exporting and shipping events. Second, the model identified a tendency for exporters and shippers to also be sellers (e.g., 1, 4, and 23 made large contributions to the prediction). Using this knowledge, the system extracted information that could not be extracted by the baseline heuristic or a traditional SRL system.

6.6 Comparison with Previous Results

In a previous study, we reported initial results for the task of implicit argument identification (Gerber and Chai 2010). This article presents two major advancements versus our prior work. First, this article presents a more rigorous evaluation set-up, which was not used in our previous study. Our previous study used fixed partitions of training, development, and testing data. As a result, feature and model parameter selections overfit the development data; we observed a 23-point difference in F1 between the development (65%) and testing (42%) partitions. The small size of the testing set also led to small sample sizes and large p-values during significance testing. The cross-validated approach reported in this article alleviated both problems. The F1 difference between training and testing was approximately 10 percentage points for all folds, and all of the data were used for testing, leading to more accurate p-values. It is not possible to directly compare the evaluation scores in the two studies; the methodology in the current article is preferable for the reasons mentioned, however.

Second, this article presents a wider range of features compared with the features described in our previous study. In particular, we experimented with corpus statistics derived from sub-corpora that were specifically tailored to the predicate instance under consideration. See, for example, Feature 13 in Appendix B, which computed PMI scores between arguments found in a custom sub-corpus of text. This feature was ranked highly by a few of the evaluation folds (see Appendix B for feature rankings).

7. Conclusions

Previous work provided a partial solution to the problem of nominals with implicit arguments (Gerber, Chai, and Meyers 2009). The model described in that work is able to accurately identify nominals that take local arguments, thus filtering out predicates whose arguments are entirely implicit. This increases standard nominal SRL performance by reducing the number of false positive argument predictions; all implicit arguments remain unidentified, however, leaving a large portion of the corresponding event structures unrecognized.

This article presents our investigation of implicit argument identification for nominal predicates. The study was based on a manually created corpus of implicit arguments, which is freely available for research purposes. Our results show that models can be trained by incorporating information from a variety of ontological and corpus-based sources. The study’s primary findings include the following:

  • 1. 

    Implicit arguments are frequent. Given the predicates in a document, there exist a fixed number of possible arguments that can be filled according to NomBank’s predicate role sets. Role coverage is defined as the fraction of these roles that are actually filled by constituents in the text. Using NomBank as a baseline, the study found that role coverage increases by 71% when implicit arguments are taken into consideration.

  • 2. 

    Implicit arguments can be automatically identified. Using the annotated data, we constructed a feature-based supervised model that is able to automatically identify implicit arguments. This model relies heavily on the traditional, single-sentence SRL structure of both nominal and verbal predicates. By unifying these sources of information, the implicit argument model provides a more coherent picture of discourse semantics than is typical in most recent work (e.g., the evaluation conducted by (Surdeanu et al. 2008)). The model demonstrates substantial gains over an informed baseline, reaching an overall F1 score of 50% and per-predicate scores in the mid-50s and mid-60s. These results are among the first for this task.

  • 3. 

    Much work remains. The study presented in the current article was very focused: Only ten different predicates were analyzed. The goal was to carefully examine the underlying linguistic properties of implicit arguments. This examination produced many features that have not been used in other SRL studies. The results are encouraging; a direct application of the model to all NomBank predicates will require a substantial annotation effort, however. This is because many of the most important features are lexicalized on the predicate being analyzed and thus cannot be generalized to novel predicates. Additional information might be extracted from VerbNet, which groups related verbs together. Features from this resource might generalize better because they apply to entire sets of verbs (and verb-based nouns). Additionally, the model would benefit from a deeper understanding of the relationships that obtain between predicates in close textual proximity. Often, predicates themselves head arguments to other predicates, and, as a result, borrow arguments from those predicates following certain patterns. The work of Blanco and Moldovan (2011) addresses this issue directly with the use of composition rules. These rules would be helpful for implicit argument identification. Lastly, it should be noted that the prediction model described in this article is quite simple. Each candidate is independently classified as filling each missing argument position, and a heuristic post-processing step is performed to arrive at the final labeling. This approach ignores the joint behavior of semantic arguments. We have performed a preliminary investigation of joint implicit argument structures (Gerber, Chai, and Bart 2011); as described in that work, however, many issues remain concerning joint implicit argument identification.

Appendix A: Role Sets for the Annotated Predicates

Listed here are the role sets for the ten predicates used in this article.

graphic

Appendix B: Implicit Argument Features

Table B.1

Features for determining whether c fills iargn of predicate p. For each mention f (denoting a filler) in the coreference chain c′, pf, and argf are the predicate and argument position of f. Unless otherwise noted, all argument positions (e.g., argn and iargn) should be interpreted as the integer label n instead of the underlying word content of the argument. The & symbol denotes concatenation; for example, a feature value of “p&iargn” for the iarg0 position of sale would be “sale-0.” Features marked with an asterisk (*) are explained in Section 4.2. Features marked with a dagger (†) require external text corpora that have been automatically processed by existing NLP components (e.g., SRL systems). The final column gives a heuristic ranking score for the features across all evaluation folds (see Section 6.2 for discussion).

#
Feature value description
Importance score
1* For every f, pf&argf&p&iargn8.2 
2* Sentence distance from c to p4.0 
3* For every f, the head word of f& the verbal form of p&iargn3.6 
4* Same as 1 except generalizing pf and p to their WordNet synsets. 3.3 
5* Same as 3 except generalizing f to its WordNet synset. 1.0 
Whether or not c and p are themselves arguments to the same predicate. 1.0 
p& the semantic head word of p's right sibling. 0.7 
Whether or not any argf and iargn have the same integer argument position. 0.7 
9* Frame element path between argf of pf and iargn of p in FrameNet (Baker, Fillmore, and Lowe 1998). 0.6 
10 Percentage of elements in c′ that are subjects of a copular for which p is the object. 0.6 
11 Whether or not the verb forms of pf and p are in the same VerbNet class and argf and iargn have the same thematic role. 0.6 
12 p& the last word of p's right sibling. 0.6 
13*† Maximum targeted PMI between argf of pf and iargn of p0.6 
14 p& the number of p's right siblings. 0.5 
15 Percentage of elements in c′ that are objects of a copular for which p is the subject. 0.5 
16 Frequency of the verbal form of p within the document. 0.5 
17 p& the stemmed content words in a one–word window around p0.5 
18 Whether or not p's left sibling is a quantifier (many, most, all, etc.). Quantified predicates tend not to take implicit arguments. 0.4 
19 Percentage of elements in c′ that are copular objects. 0.4 
20 TF cosine similarity between words from arguments of all pf and words from arguments of p0.4 
21 Whether the path defined in 9 exists. 0.4 
22 Percentage of elements in c′ that are copular subjects. 0.4 
23* For every f, the VerbNet class/role of pf/argf& the class/role of p/iargn0.4 
24 Percentage of elements in c′ that are indefinite noun phrases. 0.4 
25* p& the syntactic head word of p's right sibling. 0.3 
26 p& the stemmed content words in a two-word window around p0.3 
27*† Minimum selectional preference between any f and iargn of p. Uses the method described by (Resnik 1996) computed over an SRL-parsed version of the Penn TreeBank and Gigaword (Graff 2003) corpora. 0.3 
28 p&p's synset in WordNet. 0.3 
29† Same as 27 except using the maximum. 0.3 
30 Average per–sentence frequency of the verbal form of p within the document. 0.3 
31 p itself. 0.3 
32 p& whether p is the head of its parent. 0.3 
33*† Minimum coreference probability between argf of pf and iargn of p0.3 
34 p& whether p is before a passive verb. 0.3 
35 Percentage of elements in c′ that are definite noun phrases. 0.3 
36 Percentage of elements in c′ that are arguments to other predicates. 0.3 
37 Maximum absolute sentence distance from any f to p0.3 
38 p&p's syntactic category. 0.2 
39 TF cosine similarity between the role description of iargn and the concatenated role descriptions of all argf0.2 
40 Average TF cosine similarity between each argn of each pf and the corresponding argn of p, where ns are equal. 0.2 
41 Same as 40 except using the maximum. 0.2 
42 Same as 40 except using the minimum. 0.2 
43 p& the head of the following prepositional phrase's object. 0.2 
44 Whether any f is located between p and any of the arguments annotated by NomBank for p. When true, this feature rules out false positives because it implies that the NomBank annotators considered and ignored f as a local argument to p0.2 
45 Number of elements in c′. 0.2 
46 p& the first word of p's right sibling. 0.2 
47 p& the grammar rule that expands p's parent. 0.2 
48 Number of elements in c′ that are arguments to other predicates. 0.2 
49 Nominal form of p&iargn0.2 
50 p& the syntactic parse tree path from p to the nearest passive verb. 0.2 
51 Same as 37 except using the minimum. 0.2 
52† Same as 33 except using the average. 0.2 
53 Verbal form of p&iargn0.2 
54 p& the first word of p's left sibling. 0.2 
55 Average per-sentence frequency of the nominal form of p within the document. 0.2 
56 p& the part of speech of p's parent's head word. 0.2 
57† Same as 33 except using the maximum. 0.2 
58 Same as 37 except using the average. 0.1 
59* Minimum path length between argf of pf and iargn of p within VerbNet (Kipper 2005). 0.1 
60 Frequency of the nominal form of p within the document. 0.1 
61 p& the number of p's left siblings. 0.1 
62 p&p's parent’s head word. 0.1 
63 p& the syntactic category of p's right sibling. 0.1 
64 p&p's morphological suffix. 0.1 
65 TF cosine similarity between words from all f and words from the role description of iargn0.1 
66 Percentage of elements in c′ that are quantified noun phrases. 0.1 
67* Discourse relation whose two discourse units cover c (the primary filler) and p0.1 
68 For any f, the minimum semantic similarity between pf and p using the method described by (Wu and Palmer 1994) over WordNet (Fellbaum 1998). 0.1 
69 p& whether or not p is followed by a prepositional phrase. 0.1 
70 p& the syntactic head word of p’s left sibling. 0.1 
71 p& the stemmed content words in a three-word window around p0.1 
72 Syntactic category of c&iargn& the verbal form of p0.1 
73 Nominal form of p& the sorted integer argument indexes (the ns) from all argn of p0.1 
74 Percentage of elements in c′ that are sentential subjects. 0.1 
75 Whether or not the integer position of any argf equals that of iargn0.1 
76† Same as 13 except using the average. 0.1 
77† Same as 27 except using the average. 0.1 
78 p&p's parent’s syntactic category. 0.1 
79 p& the part of speech of the head word of p’s right sibling. 0.1 
80 p& the semantic head word of p’s left sibling. 0.1 
81† Maximum targeted coreference probability between argf of pf and iargn of p. This is a hybrid feature that calculates the coreference probability of Feature 33 using the corpus tuning method of Feature 13. 0.1 
#
Feature value description
Importance score
1* For every f, pf&argf&p&iargn8.2 
2* Sentence distance from c to p4.0 
3* For every f, the head word of f& the verbal form of p&iargn3.6 
4* Same as 1 except generalizing pf and p to their WordNet synsets. 3.3 
5* Same as 3 except generalizing f to its WordNet synset. 1.0 
Whether or not c and p are themselves arguments to the same predicate. 1.0 
p& the semantic head word of p's right sibling. 0.7 
Whether or not any argf and iargn have the same integer argument position. 0.7 
9* Frame element path between argf of pf and iargn of p in FrameNet (Baker, Fillmore, and Lowe 1998). 0.6 
10 Percentage of elements in c′ that are subjects of a copular for which p is the object. 0.6 
11 Whether or not the verb forms of pf and p are in the same VerbNet class and argf and iargn have the same thematic role. 0.6 
12 p& the last word of p's right sibling. 0.6 
13*† Maximum targeted PMI between argf of pf and iargn of p0.6 
14 p& the number of p's right siblings. 0.5 
15 Percentage of elements in c′ that are objects of a copular for which p is the subject. 0.5 
16 Frequency of the verbal form of p within the document. 0.5 
17 p& the stemmed content words in a one–word window around p0.5 
18 Whether or not p's left sibling is a quantifier (many, most, all, etc.). Quantified predicates tend not to take implicit arguments. 0.4 
19 Percentage of elements in c′ that are copular objects. 0.4 
20 TF cosine similarity between words from arguments of all pf and words from arguments of p0.4 
21 Whether the path defined in 9 exists. 0.4 
22 Percentage of elements in c′ that are copular subjects. 0.4 
23* For every f, the VerbNet class/role of pf/argf& the class/role of p/iargn0.4 
24 Percentage of elements in c′ that are indefinite noun phrases. 0.4 
25* p& the syntactic head word of p's right sibling. 0.3 
26 p& the stemmed content words in a two-word window around p0.3 
27*† Minimum selectional preference between any f and iargn of p. Uses the method described by (Resnik 1996) computed over an SRL-parsed version of the Penn TreeBank and Gigaword (Graff 2003) corpora. 0.3 
28 p&p's synset in WordNet. 0.3 
29† Same as 27 except using the maximum. 0.3 
30 Average per–sentence frequency of the verbal form of p within the document. 0.3 
31 p itself. 0.3 
32 p& whether p is the head of its parent. 0.3 
33*† Minimum coreference probability between argf of pf and iargn of p0.3 
34 p& whether p is before a passive verb. 0.3 
35 Percentage of elements in c′ that are definite noun phrases. 0.3 
36 Percentage of elements in c′ that are arguments to other predicates. 0.3 
37 Maximum absolute sentence distance from any f to p0.3 
38 p&p's syntactic category. 0.2 
39 TF cosine similarity between the role description of iargn and the concatenated role descriptions of all argf0.2 
40 Average TF cosine similarity between each argn of each pf and the corresponding argn of p, where ns are equal. 0.2 
41 Same as 40 except using the maximum. 0.2 
42 Same as 40 except using the minimum. 0.2 
43 p& the head of the following prepositional phrase's object. 0.2 
44 Whether any f is located between p and any of the arguments annotated by NomBank for p. When true, this feature rules out false positives because it implies that the NomBank annotators considered and ignored f as a local argument to p0.2 
45 Number of elements in c′. 0.2 
46 p& the first word of p's right sibling. 0.2 
47 p& the grammar rule that expands p's parent. 0.2 
48 Number of elements in c′ that are arguments to other predicates. 0.2 
49 Nominal form of p&iargn0.2 
50 p& the syntactic parse tree path from p to the nearest passive verb. 0.2 
51 Same as 37 except using the minimum. 0.2 
52† Same as 33 except using the average. 0.2 
53 Verbal form of p&iargn0.2 
54 p& the first word of p's left sibling. 0.2 
55 Average per-sentence frequency of the nominal form of p within the document. 0.2 
56 p& the part of speech of p's parent's head word. 0.2 
57† Same as 33 except using the maximum. 0.2 
58 Same as 37 except using the average. 0.1 
59* Minimum path length between argf of pf and iargn of p within VerbNet (Kipper 2005). 0.1 
60 Frequency of the nominal form of p within the document. 0.1 
61 p& the number of p's left siblings. 0.1 
62 p&p's parent’s head word. 0.1 
63 p& the syntactic category of p's right sibling. 0.1 
64 p&p's morphological suffix. 0.1 
65 TF cosine similarity between words from all f and words from the role description of iargn0.1 
66 Percentage of elements in c′ that are quantified noun phrases. 0.1 
67* Discourse relation whose two discourse units cover c (the primary filler) and p0.1 
68 For any f, the minimum semantic similarity between pf and p using the method described by (Wu and Palmer 1994) over WordNet (Fellbaum 1998). 0.1 
69 p& whether or not p is followed by a prepositional phrase. 0.1 
70 p& the syntactic head word of p’s left sibling. 0.1 
71 p& the stemmed content words in a three-word window around p0.1 
72 Syntactic category of c&iargn& the verbal form of p0.1 
73 Nominal form of p& the sorted integer argument indexes (the ns) from all argn of p0.1 
74 Percentage of elements in c′ that are sentential subjects. 0.1 
75 Whether or not the integer position of any argf equals that of iargn0.1 
76† Same as 13 except using the average. 0.1 
77† Same as 27 except using the average. 0.1 
78 p&p's parent’s syntactic category. 0.1 
79 p& the part of speech of the head word of p’s right sibling. 0.1 
80 p& the semantic head word of p’s left sibling. 0.1 
81† Maximum targeted coreference probability between argf of pf and iargn of p. This is a hybrid feature that calculates the coreference probability of Feature 33 using the corpus tuning method of Feature 13. 0.1 

Appendix C: Per-fold Implicit Argument Identification Results

Table C.1

Per-fold implicit argument identification results. Columns are defined as follows: (1) fold used for testing, (2) selected features in rank order, (3) baseline F1, (4) LibLinear cost parameter, (5) LibLinear weight for the positive class, (6) implicit argument confidence threshold, (7) discriminative F1, (8) oracle F1. A bias of 1 was used for all LibLinear models. F1, (8) oracle F1. A bias of 1 was used for all LibLinear models.



Baseline
Discriminative (LibLinear)
Oracle
Fold
Features
F1 (%)
c
w+
t
F1 (%)
F1 (%)
1, 2, 3, 11, 32, 8, 27, 22, 31, 10, 20, 53, 6, 16, 24, 40, 30, 38, 72, 69, 73, 19, 28, 42, 48, 64, 44, 36, 37, 12, 7 31.7 0.25 0.39260 47.1 86.7 
1, 3, 2, 4, 17, 13, 28, 11, 6, 18, 25, 12, 56, 29, 16, 53, 41, 31, 46, 10, 7, 51, 15, 22 32 0.25 256 0.80629 51.5 86.9 
4, 3, 2, 8, 7, 6, 59, 20, 9, 62, 37, 39, 41, 19, 10, 15, 11, 35, 61, 44, 42, 40, 32, 30, 16, 75, 33, 24 35.3 0.25 256 0.90879 55.8 88.1 
1, 2, 5, 13, 8, 49, 6, 35, 34, 14, 15, 18, 36, 28, 20, 45, 3, 43, 24, 48, 10, 29, 12, 30, 33, 65, 31, 22, 61, 16, 27, 41, 60, 55, 64 27.8 0.25 0.38540 45.8 86.5 
1, 2, 26, 3, 4, 23, 5, 63, 55, 6, 12, 44, 42, 65, 7, 71, 18, 15, 10, 14, 52, 34, 19, 24, 50, 58 25.8 0.125 1024 0.87629 45.9 88 
1, 3, 2, 14, 23, 38, 25, 39, 16, 6, 21, 68, 70, 58, 9, 22, 18, 31, 60, 10, 64, 15, 66, 19, 30, 51, 56, 28 34.8 0.25 256 0.87759 55.4 90.8 
1, 2, 4, 3, 47, 54, 43, 7, 33, 9, 67, 24, 36, 50, 40, 12, 21 22.9 0.25 256 0.81169 46.3 87.4 
1, 3, 2, 4, 9, 7, 14, 12, 6, 46, 30, 18, 19, 36, 48, 42, 37, 45, 60, 56, 61, 51, 15, 10, 41, 40, 25, 31, 11, 39, 62, 69, 34, 16, 33, 8, 38, 20, 78, 44, 55, 80, 53, 50, 52, 49, 24, 28, 57 27.1 0.0625 512 0.92019 47.4 87.2 
1, 5, 2, 4, 3, 21, 27, 10, 15, 9, 57, 35, 16, 25, 37, 33, 45, 24, 46, 29, 19, 34, 51, 50, 22, 48, 32, 11, 12, 58, 41, 8, 76, 18, 30, 40, 77, 6, 66, 44, 43, 79, 81, 20 23 0.0625 32 0.67719 54.1 85.5 
10 4, 3, 2, 17, 1, 13, 29, 12, 11, 52, 10, 15, 6, 16, 9, 22, 7, 21, 57, 19, 74, 34, 45, 20, 66 28.4 0.0625 512 0.89769 53.2 88.5 
(1) (2) (3) (4) (5) (6) (7) (8) 


Baseline
Discriminative (LibLinear)
Oracle
Fold
Features
F1 (%)
c
w+
t
F1 (%)
F1 (%)
1, 2, 3, 11, 32, 8, 27, 22, 31, 10, 20, 53, 6, 16, 24, 40, 30, 38, 72, 69, 73, 19, 28, 42, 48, 64, 44, 36, 37, 12, 7 31.7 0.25 0.39260 47.1 86.7 
1, 3, 2, 4, 17, 13, 28, 11, 6, 18, 25, 12, 56, 29, 16, 53, 41, 31, 46, 10, 7, 51, 15, 22 32 0.25 256 0.80629 51.5 86.9 
4, 3, 2, 8, 7, 6, 59, 20, 9, 62, 37, 39, 41, 19, 10, 15, 11, 35, 61, 44, 42, 40, 32, 30, 16, 75, 33, 24 35.3 0.25 256 0.90879 55.8 88.1 
1, 2, 5, 13, 8, 49, 6, 35, 34, 14, 15, 18, 36, 28, 20, 45, 3, 43, 24, 48, 10, 29, 12, 30, 33, 65, 31, 22, 61, 16, 27, 41, 60, 55, 64 27.8 0.25 0.38540 45.8 86.5 
1, 2, 26, 3, 4, 23, 5, 63, 55, 6, 12, 44, 42, 65, 7, 71, 18, 15, 10, 14, 52, 34, 19, 24, 50, 58 25.8 0.125 1024 0.87629 45.9 88 
1, 3, 2, 14, 23, 38, 25, 39, 16, 6, 21, 68, 70, 58, 9, 22, 18, 31, 60, 10, 64, 15, 66, 19, 30, 51, 56, 28 34.8 0.25 256 0.87759 55.4 90.8 
1, 2, 4, 3, 47, 54, 43, 7, 33, 9, 67, 24, 36, 50, 40, 12, 21 22.9 0.25 256 0.81169 46.3 87.4 
1, 3, 2, 4, 9, 7, 14, 12, 6, 46, 30, 18, 19, 36, 48, 42, 37, 45, 60, 56, 61, 51, 15, 10, 41, 40, 25, 31, 11, 39, 62, 69, 34, 16, 33, 8, 38, 20, 78, 44, 55, 80, 53, 50, 52, 49, 24, 28, 57 27.1 0.0625 512 0.92019 47.4 87.2 
1, 5, 2, 4, 3, 21, 27, 10, 15, 9, 57, 35, 16, 25, 37, 33, 45, 24, 46, 29, 19, 34, 51, 50, 22, 48, 32, 11, 12, 58, 41, 8, 76, 18, 30, 40, 77, 6, 66, 44, 43, 79, 81, 20 23 0.0625 32 0.67719 54.1 85.5 
10 4, 3, 2, 17, 1, 13, 29, 12, 11, 52, 10, 15, 6, 16, 9, 22, 7, 21, 57, 19, 74, 34, 45, 20, 66 28.4 0.0625 512 0.89769 53.2 88.5 
(1) (2) (3) (4) (5) (6) (7) (8) 

Acknowledgements

We would like to thank the anonymous reviewers for their many insightful comments and suggestions. This work was partially supported by NSF grants IIS-0347548 and IIS-0840538.

Notes

1 

The Securities and Exchange Commission (SEC) is responsible for enforcing investment laws in the United States.

2 

NomBank annotates arguments in the noun phrase headed by the predicate as well as arguments brought in by so-called support verb structures. See Meyers (2007) for details.

3 

This article builds on our previous work (Gerber and Chai 2010).

4 

Identification of the implicit patient in Example (17) (the ball) should be sensitive to the phenomenon of sense anaphora. If Example (16) was changed to “a ball,” then we would have no implicit patient in Example (17).

5 

See Appendix A for the list of role sets used in this study.

6 

We used OpenNLP for coreference identification: http://opennlp.sourceforge.net.

7 

Features were ranked according to the order in which they were selected during feature selection (Section 5.3 for details).

8 

http://verbs.colorado.edu/semlink.

10 

The threshold t is learned from the training data. The learning mechanism is explained in the following section.

11 

Our evaluation methodology differs slightly from that of Ruppenhofer et al. (2010) in that we use the Dice metric to compute precision and recall, whereas Ruppenhofer et al. reported the Dice metric separately from exact-match precision and recall.

12 

See Appendix Table C.1 for a per-fold listing of features and model parameters.

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Author notes

*

151 Engineer’s Way, University of Virginia, Charlottesville, VA 22904. E-mail: matt.gerber@virginia.edu.

**

3115 Engineering Building, Michigan State University, East Lansing, MI 48824. E-mail: jchai@cse.msu.edu.