Improved Learning of Chinese Word Embeddings with Semantic Knowledge

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9427)

Abstract

While previous studies show that modeling the minimum meaning-bearing units (characters or morphemes) benefits learning vector representations of words, they ignore the semantic dependencies across these units when deriving word vectors. In this work, we propose to improve the learning of Chinese word embeddings by exploiting semantic knowledge. The basic idea is to take the semantic knowledge about words and their component characters into account when designing composition functions. Experiments show that our approach outperforms two strong baselines on word similarity, word analogy, and document classification tasks.

Keywords

Word embeddings CBOW Semantic knowledge 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Technology, State Key Lab on Intelligent Technology and Systems, National Lab for Information Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Jiangsu Collaborative Innovation Center for Language AbilityJiangsu Normal UniversityXuzhouChina

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