Treebank grammar techniques for non-projective dependency parsing

Kuhlmann, Marco; Satta, Giorgio

doi:10.3115/1609067.1609120

Cited by 26 publications

(38 citation statements)

References 13 publications

(15 reference statements)

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“…More specifically, our statistics concern the training sections of the treebanks that were set off for the task. For similar results on other data sets, see Kuhlmann and Nivre (2006), Havelka (2007), and Maier and Lichte (2011).…”

Section: Coverage On Dependency Treebankssupporting

confidence: 73%

Mildly Non-Projective Dependency Grammar

Kuhlmann

2013

Computational Linguistics

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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

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Section: Coverage On Dependency Treebankssupporting

confidence: 73%

Mildly Non-Projective Dependency Grammar

Kuhlmann

2013

Computational Linguistics

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“…Exact inference for parsing models that allow non-projective trees is NP hard, except under very restricted independence assumptions (Neuhaus and Bröker, 1997;McDonald and Satta, 2007). There is recent work on algorithms that can cope with important subsets of all nonprojective trees in polynomial time (Kuhlmann and Satta, 2009;Gómez-Rodríguez et al, 2009), but the time complexity is at best O(n 6 ), which can be problematic in practical applications. Even the best algorithms for deterministic parsing run in quadratic time, rather than linear (Nivre, 2008a), unless restricted to a subset of non-projective structures as in Attardi (2006) and Nivre (2007).…”

Section: Introductionmentioning

confidence: 99%

Non-projective dependency parsing in expected linear time

Nivre¹

2009

Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural

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We present a novel transition system for dependency parsing, which constructs arcs only between adjacent words but can parse arbitrary non-projective trees by swapping the order of words in the input. Adding the swapping operation changes the time complexity for deterministic parsing from linear to quadratic in the worst case, but empirical estimates based on treebank data show that the expected running time is in fact linear for the range of data attested in the corpora. Evaluation on data from five languages shows state-of-the-art accuracy, with especially good results for the labeled exact match score.

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“…We focus on learning regular expressions with interleaving (shuffle), denoted by RE(&). Since RE(&) are widely used in various areas of computer science [4], including XML database systems [19,14,34], complex event processing [33], system verification [10,21,23], plan recognition [26] and natural language processing [27,39].…”

Section: Introductionmentioning

confidence: 99%

An effective algorithm for learning single occurrence regular expressions with interleaving

Chen

Zhang

et al. 2019

Proceedings of the 23rd International Database Applications &Amp; Engineering Symposium on - IDEAS '19

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The advantages offered by the presence of a schema are numerous. However, many XML documents in practice are not accompanied by a (valid) schema, making schema inference an attractive research problem. The fundamental task in XML schema learning is inferring restricted subclasses of regular expressions. Most previous work either lacks support for interleaving or only has limited support for interleaving. In this paper, we first propose a new subclass Single Occurrence Regular Expressions with Interleaving (SOIRE), which has unrestricted support for interleaving. Then, based on single occurrence automaton and maximum independent set, we propose an algorithm iSOIRE to infer SOIREs. Finally, we further conduct a series of experiments on real datasets to evaluate the effectiveness of our work, comparing with both ongoing learning algorithms in academia and industrial tools in real-world. The results reveal the practicability of SOIRE and the effectiveness of iSOIRE, showing the high preciseness and conciseness of our work.

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Treebank grammar techniques for non-projective dependency parsing

Cited by 26 publications

References 13 publications

Mildly Non-Projective Dependency Grammar

Mildly Non-Projective Dependency Grammar

Non-projective dependency parsing in expected linear time

An effective algorithm for learning single occurrence regular expressions with interleaving

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