Learning Word Sense Embeddings from Word Sense Definitions

摘要：

　　Word embeddings play a significant role in many modern NLP systems.Since learning one representation per word is problematic for polysemous words and homonymous words,researchers propose to use one embedding per word sense.Their approaches mainly train word sense embeddings on a corpus.In this paper,we propose to use word sense definitions to learn one embedding per word sense.Experimental results on word similarity tasks and a word sense disambiguation task show that word sense embeddings produced by our approach are of high quality.

关键词： Word sense embedding RNN WordNet

作者: Qi Li Tianshi Li 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: 1-12

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）