Earticle

초록

영어

Sentiment classification task has attracted considerable interest as sentiment information is crucial for many natural language processing (NLP) applications. The goal of sentiment classification is to predict the overall emotional polarity of a given text. Previous work has demonstrate the remarkable performance of Convolutional Neural Network (CNN). However, nearly all this work assumes a single word embedding for each word type, ignoring polysemy and thus inevitably casting negative impact on the downstream tasks. We extend the Skip-gram model to learn multiple sense embeddings for the word types, catering to introduce sense-based embeddings for CNN during sentiment classification. Instead of using the pipeline method to learn multiple sense embeddings of a word type, the sense discrimination and sense embedding learning for each word type are performed jointly based upon the semantics of its contextual words. We validate the effectiveness of the method on the commonly used datasets. Experiment results show that our method are able to improve the quality of sentiment classification when comparing with several competitive baselines.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Sentiment Analysis
  2.2. Learning Distributed Representation
 3. Our Method
 4. Experiment
  4.1. Setup
  4.2. Result
 5. Conclusion
 References

키워드

저자

• Zhijian Cui [ Department of Cognitive Science, Xiamen University, Xiamen, P.R. China ]
• Xiaodong Shi [ Department of Cognitive Science, Xiamen University, Xiamen, P.R. China ] Corresponding Author
• Yidong Chen [ Department of Cognitive Science, Xiamen University, Xiamen, P.R. China ]
• Yinmei Guo [ Intellectual property Publishing House ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605