文献类型：期刊文献

英文题名：Self-Supervised Synonym Extraction from the Web

作者：Hu, Fanghuai[1];Shao, Zhiqing[1];Ruan, Tong[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2015

卷号：31

期号：3

起止页码：1133

外文期刊名：JOURNAL OF INFORMATION SCIENCE AND ENGINEERING

收录：;EI(收录号：20152701003117);WOS:【SCI-EXPANDED(收录号:WOS:000355962400020)】；

基金：This research is supported by the National Science and Technology Pillar Program of China under Grant No. 2013BAH11F03 and National Nature Science Foundation of China under Grant No. 61003126.

语种：英文

外文关键词：synonym extraction; self-supervised learning; sequential labeling; pattern; encyclopedia

摘要：Current synonym extraction methods work in a "closed" way. Given the problem word and set of target words, researchers have to choose words synonymous with the problem word using features such as lexical patterns and distributional similarities. This paper tries to discover synonyms in an "open" way and presents a synonym extraction framework based on self-supervised learning. We first analysis the nature of the open method and argue that a trained pattern-independent model for synonym extraction is feasible. We then model the extraction of synonyms from sentences as a sequential labeling problem and automatically generate labeled training samples by using structured knowledge from online encyclopedias and some generic heuristic rules. Finally, we train some Conditional Random Field (CRF) models and use them to extract synonyms from the web. We successfully extract more than 20 million facts, which contain 826,219 distinct pairs of synonyms.