Exploiting full parsing information to label semantic roles using an ensemble of ME and SVM via Integer linear programming

Tzong Han Tsai, Chia Wei Wu, Yu Chun Lin, Wen Lian Hsu

Research output: Contribution to conferencePaperpeer-review

14 Scopus citations

Abstract

In this paper, we propose a method that exploits full parsing information by representing it as features of argument classification models and as constraints in integer linear learning programs. In addition, to take advantage of SVM-based and Maximum Entropy-based argument classification models, we incorporate their scoring matrices, and use the combined matrix in the above-mentioned integer linear programs. The experimental results show that full parsing information not only increases the F-score of argument classification models by 0.7%, but also effectively removes all labeling inconsistencies, which increases the F-score by 0.64%. The ensemble of SVM and ME also boosts the F-score by 0.77%. Our system achieves an F-score of 76.53% in the development set and 76.38% in Test WSJ.

Original languageEnglish
Pages233-236
Number of pages4
DOIs
StatePublished - 2005
Event9th Conference on Computational Natural Language Learning, CoNLL 2005 - Ann Arbor, MI, United States
Duration: 29 Jun 200530 Jun 2005

Conference

Conference9th Conference on Computational Natural Language Learning, CoNLL 2005
Country/TerritoryUnited States
CityAnn Arbor, MI
Period29/06/0530/06/05

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