该文实现了一个基于机器学习的指代消解平台,并在此基础上着重研究了语义角色特征对指代消解的影响。该文使用ASSERT①语义角色标注系统得到语义角色标注信息,然后在原型系统的基础上加入语义角色特征。为了分析语义角色特征对指代消解的影响,该文还分析了语义角色特征和指代链特征以及代词细化特征的结合对系统的影响。通过把先行语和照应语在句子中所作的语义角色特征加入机器学习系统中进行研究,该文发现语义角色特征能够显著提高系统的性能,特别是对代词的消解有很好的效果。在ACE 2003 NWIRE基准语料上的所有类型名词短语的指代消解测试表明,召回率提高了3.4%,F值提高了1.8%。
Abstract
This paper proposes a machine learning-based approach to coreference resolution with special focus on the semantic role labeling information of the anaphor and the antecedent candidate. We first combine the baseline system with semantic role features which are acquired from ASSERT system. Furthermore, we analyze the integration of semantic role feature with detailed pronoun type knowledge, which suggests that incorporating semantic role information of anaphor and its antecedent candidates is beneficial to coreference resolution, especially to pronouns. Evaluation on the ACE-2003 NWIRE benchmark corpus shows that systems with proper handling of semantic role information achieves significant improvements of 3.4% in recall and 1.8% in F-measure respectively.
关键词
计算机应用 /
中文信息处理 /
指代消解 /
语义角色 /
指代链 /
机器学习
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Key words
computer application /
Chinese information processing /
anaphora resolution /
semantic roles /
coreference chain /
machine learning
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参考文献
[1] 王厚峰. 指代消解的基本方法和实现技术[J]. 中文信息学报,2002,(6): 9-17.
[2] Wee.Meng Soon, Hwee Tou Ng and Daniel Chung Yong lim. A machine learning approach to coreference resolution of noun phrase[J]. Computational Linguistics,2001,27(4): 521-544.
[3] Vincent Ng and Claire Cardie. Improving machine learning approaches to coreference resolution[C]// Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics,2002.
[4] Yang X.F., Su J., Zhou G.D. and Tan C.L. 2004. Improving pronoun resolution by incorporating coreferential information of candidates[C]//ACL’2004: 127-134, Barcelona, Spain, 21-26, July,2004 .
[5] Yang X.F.,Zhou G.D., Su J. and Tan C.L. 2003. Coreference Resolution Using Competition Learning Approach[C]//ACL’2003: 176-183, Sapporo, Japan, 7-12, July,2003.
[6] Yang X.F., Su J. and Tan C.L. 2006, Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge[C]//ACL’ 2006:41-48, Sydney,July.2006.
[7] Zhou G.D. and Su J. 2004. A high-performance coreference resolution system using a multi-agent strategy[C]//COLING’2004:522-528. 23-27, Aug, 2004, Geneva, Switzerland.
[8] Zhou GD. and Su J. 2002. Named Entity Recognition using an HNMM-based Chunk Tagger[C]//ACL’ 2002: 473-480, Philadelphia, July,2002.
[9] Grosz, A. JoShi, and S. Weinstein. 1983. Providing a unified account of definite noun phrases in discourse[C]//Proceedings of the 21st Annual meeting of the Association for Computational Linguistics, 44-45.
[10] Grosz, A. JoShi and S. Weinstein. 1995. Centering: a framework for modeling the local coherence of discourse[J]. Computational Linguistics, 21(2): 203-225.
[11] Vincent Ng. Machine Learning for Coreference Resolution[C]//From Local Classification to Global Ranking. ACL, 157-164, Ann Arbor, June, 2005.
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脚注
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基金
国家自然科学基金资助项目(60673041);国家863高技术研究发展计划资助项目(2006AA01Z147)
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