Head-driven transition-based parsing with top-down prediction

Authors:
Katsuhiko Hayashi;Taro Watanabe;Masayuki Asahara;Yuji Matsumoto
Affiliations:
Nara Institute of Science and Technology, Ikoma, Nara, Japan;National Institute of Information and Communications Technology, Sorakugun, Kyoto, Japan;National Institute for Japanese Language and Linguistics, Tachikawa, Tokyo, Japan;Nara Institute of Science and Technology, Ikoma, Nara, Japan
Venue:
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Year:
2012

Citing 21
Cited 0

An efficient probabilistic context-free parsing algorithm that computes prefix probabilities

Computational Linguistics
An efficient context-free parsing algorithm

Communications of the ACM
The theory of parsing, translation, and compiling

The theory of parsing, translation, and compiling
Weighted deductive parsing and Knuth's algorithm

Computational Linguistics
Efficient parsing for bilexical context-free grammars and head automaton grammars

ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Inductive Dependency Parsing (Text, Speech and Language Technology)

Inductive Dependency Parsing (Text, Speech and Language Technology)
Incremental parsing with the perceptron algorithm

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A deterministic word dependency analyzer enhanced with preference learning

COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Sorting Out Dependency Parsing

GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Algorithms for deterministic incremental dependency parsing

Computational Linguistics
Experiments with a multilanguage non-projective dependency parser

CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Incrementality in deterministic dependency parsing

IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
A tale of two parsers: investigating and combining graph-based and transition-based dependency parsing using beam-search

EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
An efficient algorithm for easy-first non-directional dependency parsing

HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Efficient third-order dependency parsers

ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Dynamic programming for linear-time incremental parsing

ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Tree-based deterministic dependency parsing: an application to Nivre's method

ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Dual decomposition for parsing with non-projective head automata

EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Transition-based dependency parsing with rich non-local features

HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Third-order variational reranking on packed-shared dependency forests

EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel top-down head-driven parsing algorithm for data-driven projective dependency analysis. This algorithm handles global structures, such as clause and coordination, better than shift-reduce or other bottom-up algorithms. Experiments on the English Penn Treebank data and the Chinese CoNLL-06 data show that the proposed algorithm achieves comparable results with other data-driven dependency parsing algorithms.