We contribute to the discussion on parsing performance in NLP by introducing a measurement that evaluates the differences between the distributions of edge displacement (the directed distance of edges) seen in training and test data. We hypothesize that this measurement will be related to differences observed in parsing performance across treebanks. We motivate this by building upon previous work and then attempt to falsify this hypothesis by using a number of statistical methods. We establish that there is a statistical correlation between this measurement and parsing performance even when controlling for potential covariants. We then use this to establish a sampling technique that gives us an adversarial and complementary split. This gives an idea of the lower and upper bounds of parsing systems for a given treebank in lieu of freshly sampled data. In a broader sense, the methodology presented here can act as a reference for future correlation-based exploratory work in NLP.

In the last decade, various restricted classes of non-projective dependency trees have been proposed with the goal of achieving a good tradeoff between parsing efficiency and coverage of the syntactic structures found in natural languages. We perform an extensive study measuring the coverage of a wide range of such classes on corpora of 30 languages under two different syntactic annotation criteria. The results show that, among the currently known relaxations of projectivity, the best tradeoff between coverage and computational complexity of exact parsing is achieved by either 1-endpoint-crossing trees or MH k trees, depending on the level of coverage desired. We also present some properties of the relation of MH k trees to other relevant classes of trees.

Transition-based parsing is a widely used approach for dependency parsing that combines high efficiency with expressive feature models. Many different transition systems have been proposed, often formalized in slightly different frameworks. In this article, we show that a large number of the known systems for projective dependency parsing can be viewed as variants of the same stack-based system with a small set of elementary transitions that can be composed into complex transitions and restricted in different ways. We call these systems divisible transition systems and prove a number of theoretical results about their expressivity and complexity. In particular, we characterize an important subclass called efficient divisible transition systems that parse planar dependency graphs in linear time. We go on to show, first, how this system can be restricted to capture exactly the set of planar dependency trees and, secondly, how the system can be generalized to k -planar trees by making use of multiple stacks. Using the first known efficient test for k -planarity, we investigate the coverage of k -planar trees in available dependency treebanks and find a very good fit for 2-planar trees. We end with an experimental evaluation showing that our 2-planar parser gives significant improvements in parsing accuracy over the corresponding 1-planar and projective parsers for data sets with non-projective dependency trees and performs on a par with the widely used arc-eager pseudo-projective parser.

We introduce dependency parsing schemata, a formal framework based on Sikkel's parsing schemata for constituency parsers, which can be used to describe, analyze, and compare dependency parsing algorithms. We use this framework to describe several well-known projective and non-projective dependency parsers, build correctness proofs, and establish formal relationships between them. We then use the framework to define new polynomial-time parsing algorithms for various mildly non-projective dependency formalisms, including well-nested structures with their gap degree bounded by a constant k in time O(n 5+2k ) , and a new class that includes all gap degree k structures present in several natural language treebanks (which we call mildly ill-nested structures for gap degree k ) in time O(n 4+3k ) . Finally, we illustrate how the parsing schema framework can be applied to Link Grammar, a dependency-related formalism.