个人信息Personal Information
副教授
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:软件学院、国际信息与软件学院
学科:计算机软件与理论. 软件工程
电子邮箱:xyliu@dlut.edu.cn
Multiple feature-sets method for dependency parsing
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论文类型:会议论文
发表时间:2014-07-13
收录刊物:EI、CPCI-S、Scopus
页面范围:57-62
关键字:dependency parsing; semi-supervised methods
摘要:This paper presents a simple and effective approach to improve dependency parsing by exploiting multiple feature-sets. Traditionally, features are extracted by applying the feature templates to all the word pairs(first-order features) and word tuples(second-order features). In this pa per, we show that exploiting different feature templates for different word pairs and word tuples achieves significant improvement over baseline parsers. First, we train a text chunker using a freely available implementation of the first-order linear conditional random fields model. Then we build a clause-chunk tree for a given sentence based on chunking information and punctuation marks. Finally, we extract features for dependency parsing according to multiple feature-sets. We extend the projective parsing algorithms of McDonald[20] and Carreras[1] for our case, experimental results show that our approach significantly outperform the baseline systems without increasing complexity. Given correct chunking information, we improve from baseline accuracies of 91.36% and 92.20% to 93.19% and 93.89%, respectively.