DocumentCode
2541452
Title
Word Sense Discrimination Based on Word-Sense Category Extending
Author
Fan, Dongmei ; Lu, Zhimao ; Cheng, Guobin ; Zhang, Rubo
Author_Institution
Pattern Recognition & Natural Comput. Lab., Harbin Eng. Univ., Harbin, China
fYear
2009
fDate
4-6 Nov. 2009
Firstpage
1
Lastpage
5
Abstract
The problem of ambiguous word sense poses lots of difficulties for language automatic understanding. And the research of word sense discrimination is applied to the resolution of this problem. Statistics are booming in researching this problem. While due to limitation of the scale of training corpus, the method of statistical word sense discrimination can not attain satisfying results yet. Therefore, under condition that only limit scale corpus is available, how to improve the efficiency and effectiveness of statistical learning method is a hotspot in supervised word sense recognition research. On the basis of the concept of word sense category, a new word sense discrimination method using word sense category extending is proposed. Experiment results show that the proposed method can effectively improve the accuracy of word sense discrimination as the training corpus is not enlarged.
Keywords
computational linguistics; natural language processing; pattern classification; pattern clustering; statistical analysis; unsupervised learning; ambiguous statistical word sense discrimination; automatic natural language understanding; classification task; clustering task; computational linguistics; statistical learning method; supervised word sense recognition research; training corpus; unsupervised learning; word-sense category extending; Artificial intelligence; Natural languages; Pattern recognition; Statistical learning; Statistics; Supervised learning; Surface acoustic waves; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4199-0
Type
conf
DOI
10.1109/CCPR.2009.5344018
Filename
5344018
Link To Document