Improving Word Vector with Prior Knowledge in Semantic Dictionary

摘要：

　　Using low dimensional vector space to represent words has been very effective in many NLP tasks.However,it doesnt work well when faced with the problem of rare and unseen words.In this paper,we propose to leverage the knowledge in semantic dictionary in combination with some morphological information to build an enhanced vector space.We get an improvement of 2.3%over the state-of-the-art Heidel Time system in temporal expression recognition,and obtain a large gain in other name entity recognition(NER)tasks.The semantic dictionary Hownet alone also shows promising results in computing lexical similarity.

关键词： rare words semantic dictionary morphological information word embedding

作者: Wei Li Yunfang Wu Xueqiang Lv

作者单位: Key Laboratory of Computational Linguistics,Peking University,Beijing Beijing Key Laboratory of Internet Culture and Digital Dissemination Research,Beijing

会议类型: 国际会议

会议名称: 第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

会议地点: 昆明

会议语种:英文

页码: 1-9

在线出版日期: 2016-12-02（万方平台首次上网日期，不代表论文的发表时间）