Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Using Text Corpora for Understanding Polysemy in Bangla
LEC '02 Proceedings of the Language Engineering Conference (LEC'02)
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Combining weak knowledge sources for sense disambiguation
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A collocation-based WSD model: RFR-SUM
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Hi-index | 0.00 |
Word sense disambiguation plays an important role in natural language processing, such as information retrieval, text summarization, machine translation etc. This paper proposes a corpus-based Chinese word sense disambiguation approach using HowNet. The method is based on the co-occurrence frequency between the relatives (such as synonym, antonymy, meronymy) of target word and each word in the context. Further, domains have been used to characterize the senses of polysemous word. To our knowledge, this is the first time a Chinese word sense disambiguation method using domain knowledge is reported. The accuracy is 73.2% at present. The experimental result shows that the method is very promising for Chinese word sense disambiguation.