NDLI: Semi-supervised Chinese contextual polarity classification with automatic feature selection

Content Provider	IEEE Xplore Digital Library
Author	Ge Xu Houfeng Wang
Copyright Year	2011
Description	Author affiliation: Key Laboratory of Computational Linguistics (Peking University), Ministry of Education, Beijing, 100871, China (Ge Xu; Houfeng Wang)
Abstract	Common approaches to the tasks of sentiment analysis start with a list of words with prior polarities, which are context-free. However, a word can exhibit different polarities in different contexts, which are termed as contextual polarities. In this paper, viewing polarities of words as properties of word senses, we treat the Chinese contextual polarity classification as word sense disambiguation (WSD), and manually labeled a Chinese dataset for training and testing. Due to the insufficiency of labeled data, semi-supervised methods are adopted; to find the effective features for the contextual polarity classification, two automatic feature selection algorithms are proposed. We combine the semi-supervised methods with automatic feature selection algorithms in order to utilize the strengths of both. The experimental results show that the semi-supervised methods, automatic feature selection, and the combination of both help to improve the Chinese contextual polarity classification above a supervised baseline model.
Starting Page	1019
Ending Page	1023
File Size	290840
Page Count	5
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022245
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Accuracy Machine learning word sense disambiguation Classification algorithms Computational linguistics semi-supervised learning contextual polarity automatic feature selection Niobium prior polarity
Content Type	Text
Resource Type	Article

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