论文名称：Lowly lexicalized Chinese dependency analyzer
论文类型：会议论文
收录刊物：CPCI-S
页面范围：29-34
关键字：Chinese dependency analysis; Nivre's algorithm; support vector machines; preference learning; lowly lexicalized
摘要：This paper improved the long-distance dependency model and root node finder. Support vector machines (SVMs) are applied to identify Chinese dependency structure and preference learning (PL) is applied to find root node. This paper also discussed the effectiveness of lexicalization in root node finding and dependency analyzing. Experiments using the Harbin University of Technology Corpus show that minimal or low lexicalization is sufficient for root node finding accuracy and dependency analyzing accuracy. And low lexicalization could improve the finding and analyzing efficiency.
发表时间：2007-01-01