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 :
بازگشت