CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 复杂系统研究
Different Contexts Lead to Different Word Embeddings
Hu WP(胡文鹏); Zhang JJ(张家俊); Zheng N(郑楠)
Source PublicationProceedings of COLING 2016: Technical Papers
2016
Abstract

Recent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering. However, most of these models assume a single vector per word type without considering polysemy and homonymy. In this paper, we present an extension to the CBOW model which not only improves the quality of embeddings but also makes embeddings suitable for polysemy. It differs from most of the related work in that it learns one semantic center embedding and one context bias instead of training multiple embeddings per word type. Different context leads to different bias which is defined as the weighted average embeddings of local context. Experimental results on similarity task and analogy task show that the word representations learned by the proposed method outperform the competitive baselines. 


Indexed ByEI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14714
Collection复杂系统管理与控制国家重点实验室_复杂系统研究
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Hu WP,Zhang JJ,Zheng N. Different Contexts Lead to Different Word Embeddings[J]. Proceedings of COLING 2016: Technical Papers,2016.
APA Hu WP,Zhang JJ,&Zheng N.(2016).Different Contexts Lead to Different Word Embeddings.Proceedings of COLING 2016: Technical Papers.
MLA Hu WP,et al."Different Contexts Lead to Different Word Embeddings".Proceedings of COLING 2016: Technical Papers (2016).
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