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发布时间：2019-03-11

论文类型：会议论文

发表时间：2014-07-13

收录刊物：Scopus、CPCI-S、EI

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.