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个人信息Personal Information
教授
博士生导师
硕士生导师
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:计算机科学与技术学院
学科:计算机应用技术. 计算机软件与理论
办公地点:创新大厦A930
电子邮箱:lils@dlut.edu.cn
Coreference Resolution in Biomedical Texts
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论文类型:会议论文
发表时间:2014-01-01
收录刊物:CPCI-S、Scopus
页面范围:12-14
关键字:coreference resolution; machine learning; hybrid method; rule-based
摘要:Coreference resolution recently plays a more and more important role for many natural language processing tasks. In this paper, we propose two methods for the biomedical coreference resolution. One is the single machine learning method (SVM ranker-learning algorithm) which selects appropriate features for the pronoun and noun phrase coreference resolution respectively. The other one is the hybrid method which adopts the rule-based method or the machine learning method for relative pronouns, non-relative pronouns and noun phrases coreference resolution respectively. Experiments are carried out on Biomedical Natural Language Process Shared Task (BioNLP-ST) (1)2011 coreference resolution corpus. In the first method (the single machine learning method), the F-score is 49.36%, higher than that using the same method with the features in the Reconcile system by 10.06%. In the second method (the hybrid method), the F-score is 68.61%, higher than that of the currently best system by 1.21%.