In this article we explore the syntactic and semantic properties of prepositions in the context of the semantic interpretation of nominal phrases and compounds. We investigate the problem based on cross-linguistic evidence from a set of six languages: English, Spanish, Italian, French, Portuguese, and Romanian. The focus on English and Romance languages is well motivated. Most of the time, English nominal phrases and compounds translate into constructions of the form N P N in Romance languages, where the P (preposition) may vary in ways that correlate with the semantics. Thus, we present empirical observations on the distribution of nominal phrases and compounds and the distribution of their meanings on two different corpora, based on two state-of-the-art classification tag sets: Lauer's set of eight prepositions and our list of 22 semantic relations. A mapping between the two tag sets is also provided. Furthermore, given a training set of English nominal phrases and compounds along with their translations in the five Romance languages, our algorithm automatically learns classification rules and applies them to unseen test instances for semantic interpretation. Experimental results are compared against two state-of-the-art models reported in the literature.

An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part-whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part-whole relations. A difficulty is that these patterns also encode other semantic relations, and a learning method is necessary to discriminate whether or not a pattern contains a part-whole relation. A large set of training examples have been annotated and fed into a specialized learning system that learns classification rules. The rules are learned through an iterative semantic specialization (ISS) method applied to noun phrase constituents. Classification rules have been generated this way for different patterns such as genitives, noun compounds, and noun phrases containing prepositional phrases to extract part-whole relations from them. The applicability of these rules has been tested on a test corpus obtaining an overall average precision of 80.95% and recall of 75.91%. The results demonstrate the importance of word sense disambiguation for this task. They also demonstrate that different lexico-syntactic patterns encode different semantic information and should be treated separately in the sense that different clarification rules apply to different patterns.