Mildly Non-Projective Dependency Grammar

Abstract

Syntactic representations based on word-to-word dependencies have a long-standing tradition in descriptive linguistics, and receive considerable interest in many applications. Nevertheless, dependency syntax has remained somewhat of an island from a formal point of view. Moreover, most formalisms available for dependency grammar are restricted to projective analyses, and thus not able to support natural accounts of phenomena such as wh-movement and cross–serial dependencies. In this article we present a formalism for non-projective dependency grammar in the framework of linear context-free rewriting systems. A characteristic property of our formalism is a close correspondence between the non-projectivity of the dependency trees admitted by a grammar on the one hand, and the parsing complexity of the grammar on the other. We show that parsing with unrestricted grammars is intractable. We therefore study two constraints on non-projectivity, block-degree and well-nestedness. Jointly, these two constraints define a class of “mildly” non-projective dependency grammars that can be parsed in polynomial time. An evaluation on five dependency treebanks shows that these grammars have a good coverage on empirical data

Large-scale evidence of dependency length minimization in 37 languages

“…Another pervasive property of languages is projectivity, the property that, in linearizations of dependency graphs, the lines connecting heads and dependents do not cross (18). Ferrer i Cancho (19) has argued that this ubiquitous property of languages arises from dependency length minimization, because orders that minimize dependency length have a small number of crossing dependencies on average.…”

Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations

“…Producing these strings requires a stack-like data structure where some number of as are pushed onto the stack so that the same number of bs can be popped from it. The hierarchical structures of natural language are widely believed to be mildly context-sensitive (Shieber, 1985;Weir, 1988;Seki et al, 1991;Joshi and Schabes, 1997;Kuhlmann, 2013), so this result shows that LSTMs are practically capable of inducing the proper data structures to handle the hierarchical structure of natural language. What remains to be seen in a general way is that LSTMs induce and use these structures when trained on natural language input, rather than artificial language input.…”

A Linear-Time Transition System for Crossing Interval Trees

“…2-Crossing Interval trees are not necessarily well-nested and can have unbounded block degree (Kuhlmann, 2013). Figure 2 shows an example of a 2-Crossing Interval tree (all crossed edges are incident to either a or b; no children are on the far side of their parent) in which the subtrees rooted at a and b are ill-nested and each has a block degree of n + 1.…”