Multilingual syntactic-semantic dependency parsing with three-stage approximate max-margin linear models

Yotaro Watanabe; Masayuki Asahara; Yuji Matsumoto

doi:10.3115/1596409.1596429

Multilingual syntactic-semantic dependency parsing with three-stage approximate max-margin linear models

Yotaro Watanabe, Masayuki Asahara, Yuji Matsumoto

INF - System Information Sciences

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

This paper describes a system for syntacticsemantic dependency parsing for multiple languages. The system consists of three parts: a state-of-the-art higher-order projective dependency parser for syntactic dependency parsing, a predicate classifier, and an argument classifier for semantic dependency parsing. For semantic dependency parsing, we explore use of global features. All components are trained with an approximate max-margin learning algorithm. In the closed challenge of the CoNLL-2009 Shared Task (Hajič et al., 2009), our system achieved the 3rd best performances for English and Czech, and the 4th best performance for Japanese.

Original language	English
Pages	114-119
Number of pages	6
DOIs	https://doi.org/10.3115/1596409.1596429
Publication status	Published - 2009
Event	13th Conference on Computational Natural Language Learning, CoNLL 2009 - Boulder, CO, United States Duration: 2009 Jun 4 → 2009 Jun 4

Other

Other	13th Conference on Computational Natural Language Learning, CoNLL 2009
Country/Territory	United States
City	Boulder, CO
Period	09/6/4 → 09/6/4

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Linguistics and Language

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10.3115/1596409.1596429

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title = "Multilingual syntactic-semantic dependency parsing with three-stage approximate max-margin linear models",

abstract = "This paper describes a system for syntacticsemantic dependency parsing for multiple languages. The system consists of three parts: a state-of-the-art higher-order projective dependency parser for syntactic dependency parsing, a predicate classifier, and an argument classifier for semantic dependency parsing. For semantic dependency parsing, we explore use of global features. All components are trained with an approximate max-margin learning algorithm. In the closed challenge of the CoNLL-2009 Shared Task (Haji{\v c} et al., 2009), our system achieved the 3rd best performances for English and Czech, and the 4th best performance for Japanese.",

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doi = "10.3115/1596409.1596429",

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AU - Watanabe, Yotaro

AU - Asahara, Masayuki

AU - Matsumoto, Yuji

PY - 2009

Y1 - 2009

N2 - This paper describes a system for syntacticsemantic dependency parsing for multiple languages. The system consists of three parts: a state-of-the-art higher-order projective dependency parser for syntactic dependency parsing, a predicate classifier, and an argument classifier for semantic dependency parsing. For semantic dependency parsing, we explore use of global features. All components are trained with an approximate max-margin learning algorithm. In the closed challenge of the CoNLL-2009 Shared Task (Hajič et al., 2009), our system achieved the 3rd best performances for English and Czech, and the 4th best performance for Japanese.

AB - This paper describes a system for syntacticsemantic dependency parsing for multiple languages. The system consists of three parts: a state-of-the-art higher-order projective dependency parser for syntactic dependency parsing, a predicate classifier, and an argument classifier for semantic dependency parsing. For semantic dependency parsing, we explore use of global features. All components are trained with an approximate max-margin learning algorithm. In the closed challenge of the CoNLL-2009 Shared Task (Hajič et al., 2009), our system achieved the 3rd best performances for English and Czech, and the 4th best performance for Japanese.

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M3 - Paper

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T2 - 13th Conference on Computational Natural Language Learning, CoNLL 2009

Y2 - 4 June 2009 through 4 June 2009

ER -

Multilingual syntactic-semantic dependency parsing with three-stage approximate max-margin linear models

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