Semantic role labeling using complete syntactic analysis

Mihai Surdeanu; Jordi Turmo

doi:10.3115/1706543.1706586

Semantic role labeling using complete syntactic analysis

Mihai Surdeanu, Jordi Turmo

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

36 Scopus citations

Abstract

In this paper we introduce a semantic role labeling system constructed on top of the full syntactic analysis of text. The labeling problem is modeled using a rich set of lexical, syntactic, and semantic attributes and learned using one-versus-all AdaBoost classifiers. Our results indicate that even a simple approach that assumes that each semantic argument maps into exactly one syntactic phrase obtains encouraging performance, surpassing the best system that uses partial syntax by almost 6%.

Original language	English (US)
Pages	221-224
Number of pages	4
DOIs	https://doi.org/10.3115/1706543.1706586
State	Published - 2005
Externally published	Yes
Event	9th Conference on Computational Natural Language Learning, CoNLL 2005 - Ann Arbor, MI, United States Duration: Jun 29 2005 → Jun 30 2005

Other

Other	9th Conference on Computational Natural Language Learning, CoNLL 2005
Country/Territory	United States
City	Ann Arbor, MI
Period	6/29/05 → 6/30/05

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Linguistics and Language

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10.3115/1706543.1706586

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title = "Semantic role labeling using complete syntactic analysis",

abstract = "In this paper we introduce a semantic role labeling system constructed on top of the full syntactic analysis of text. The labeling problem is modeled using a rich set of lexical, syntactic, and semantic attributes and learned using one-versus-all AdaBoost classifiers. Our results indicate that even a simple approach that assumes that each semantic argument maps into exactly one syntactic phrase obtains encouraging performance, surpassing the best system that uses partial syntax by almost 6%.",

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AB - In this paper we introduce a semantic role labeling system constructed on top of the full syntactic analysis of text. The labeling problem is modeled using a rich set of lexical, syntactic, and semantic attributes and learned using one-versus-all AdaBoost classifiers. Our results indicate that even a simple approach that assumes that each semantic argument maps into exactly one syntactic phrase obtains encouraging performance, surpassing the best system that uses partial syntax by almost 6%.

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Semantic role labeling using complete syntactic analysis

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