High Quality Dependency Selection from Automatic Parses

Gongye Jin, Daisuke Kawahara, Sadao Kurohashi

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

2 Citations (Scopus)

Abstract

Many NLP tasks such as question answering and knowledge acquisition are tightly dependent on dependency parsing. Dependency parsing accuracy is always decisive for the performance of subsequent tasks. Therefore, reducing dependency parsing errors or selecting high quality dependencies is a primary issue. In this paper, we present a supervised approach for automatically selecting high quality dependencies from automatic parses. Experimental results on three different languages show that our approach can effectively select high quality dependencies from the result analyzed by a dependency parser.

Original languageEnglish
Title of host publication6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Proceedings of the Main Conference
EditorsRuslan Mitkov, Jong C. Park
PublisherAsian Federation of Natural Language Processing
Pages947-951
Number of pages5
ISBN (Electronic)9784990734800
Publication statusPublished - 2013
Event6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Nagoya, Japan
Duration: 2013 Oct 14 → …

Publication series

Name6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Proceedings of the Main Conference

Conference

Conference6th International Joint Conference on Natural Language Processing, IJCNLP 2013
Country/TerritoryJapan
CityNagoya
Period13/10/14 → …

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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