Semantic dependency parsing using N-best semantic role sequences and roleset information

Joo Young Lee, Han Cheol Cho, Hae Chang Rim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we describe a syntactic and semantic dependency parsing system submitted to the shared task of CoNLL 2008. The proposed system consists of five modules: syntactic dependency parser, predicate identifier, local semantic role labeler, global role sequence candidate generator, and role sequence selector. The syntactic dependency parser is based on Malt Parser and the sequence candidate generator is based on CKY style algorithm. The remaining three modules are implemented by using maximum entropy classifiers. The proposed system achieves 76.90 of labeled F1 for the overall task, 84.82 of labeled attachment, and 68.71 of labeled F1 on the WSJ+Brown test set.

Original languageEnglish
Title of host publicationCoNLL 2008 - Proceedings of the Twelfth Conference on Computational Natural Language Learning
PublisherAssociation for Computational Linguistics (ACL)
Pages233-237
Number of pages5
ISBN (Print)1905593481, 9781905593484
DOIs
Publication statusPublished - 2008
Event12th Conference on Computational Natural Language Learning, CoNLL 2008 - Manchester, United Kingdom
Duration: 2008 Aug 162008 Aug 17

Publication series

NameCoNLL 2008 - Proceedings of the Twelfth Conference on Computational Natural Language Learning

Other

Other12th Conference on Computational Natural Language Learning, CoNLL 2008
CountryUnited Kingdom
CityManchester
Period08/8/1608/8/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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    Lee, J. Y., Cho, H. C., & Rim, H. C. (2008). Semantic dependency parsing using N-best semantic role sequences and roleset information. In CoNLL 2008 - Proceedings of the Twelfth Conference on Computational Natural Language Learning (pp. 233-237). (CoNLL 2008 - Proceedings of the Twelfth Conference on Computational Natural Language Learning). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1596324.1596366