Improved Graph-based Dependency Parsing via Hierarchical LSTM Networks

摘要：

　　In this paper,we propose a neural graph-based dependency parsing model which utilizes hierarchical LSTM networks on character level and word level to learn word representations,allowing our model to avoid the problem of limited-vocabulary and capture both distributional and compositional semantic information.Our model achieves state-of-the-art accuracy on Chinese Penn Treebank and competitive accuracy on English Penn Treebank with only first-order features.Moreover,our model shows effectiveness in recovering dependencies involving out-of-vocabulary words.

关键词： Graph-based dependency parsing Hierarchical LSTM

作者: Wenhui Wang Baobao Chang

作者单位: Key Laboratory of Computational Linguistics,Ministry of Education.School of Electronics Engineering Collaborative Innovation Center for Language Ability,Xuzhou,221009,China

会议类型: 国内会议

会议名称: 第十五届全国计算语言学学术会议(CCL2016)暨第四届基于自然标注大数据的自然语言处理国际学术研讨会(NLP-NABD-2016)

会议地点: 烟台

会议语种:英文

页码: 1-9

在线出版日期: 2016-10-14（万方平台首次上网日期，不代表论文的发表时间）