指代消解中语义角色特征的研究

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ASSERT^①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%。

Research on Semantic Role Information in Anaphora Resolution

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 coref...