DocumentCode
2349157
Title
Negation disambiguation using the maximum entropy model
Author
Zhang, Chunliang ; Fei, Xiaoxu ; Zhu, Jingbo
Author_Institution
Natural Language Lab., Northeastern Univ., Shenyang, China
fYear
2010
fDate
21-23 Aug. 2010
Firstpage
1
Lastpage
5
Abstract
Handling negation issue is of great significance for sentiment analysis. Most previous studies adopted a simple heuristic rule for sentiment negation disambiguation within a fixed context window. In this paper we present a supervised method to disambiguate which sentiment word is attached to the negator such as “(not)” in an opinionated sentence. Experimental results show that our method can achieve better performance than traditional methods.
Keywords
learning (artificial intelligence); maximum entropy methods; natural language processing; fixed context window; maximum entropy model; sentiment analysis; sentiment negation disambiguation; supervised learning method; Classification algorithms; Entropy; Gold; Machine learning; Manuals; Natural languages; Pragmatics; Negator; relation pair; sentiment negation disambiguation;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Language Processing and Knowledge Engineering (NLP-KE), 2010 International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-6896-6
Type
conf
DOI
10.1109/NLPKE.2010.5587857
Filename
5587857
Link To Document