Research on Dual Pattern of Unsupervised and Supervised Word Sense Disambiguation

Author

Wang, Yao-feng ; Zhang, Yue-jie ; Xu, Zhi-ting ; Zhang, Tao

Author_Institution

Dept. of Comput. Sci. & Eng., Fudan Univ., Shanghai

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2665

Lastpage

2669

Abstract

As an important work in the field of natural language processing, word sense disambiguation (WSD) has been a research focus since 1950. The task of WSD is very difficult to solve, and most of modern algorithms fail to reach an ideal level. The processing for WSD is to determine the sense of a polysemous word within a specific context, which involves two steps - determining all the senses for the polysemous word and selecting the appropriate sense among them. In this paper, a dual pattern of WSD based on supervised and unsupervised learning is proposed. Hence, WSD problem can be solved under different circumstances and conditions. Also, an adapted extended Lesk algorithm is established. The experiment results show that the whole quality of unsupervised and supervised WSD is satisfactory

Keywords

dictionaries; natural languages; support vector machines; unsupervised learning; word processing; adapted extended Lesk algorithm; natural language processing; polysemous word; supervised learning; support vector machine; unsupervised learning; word sense disambiguation; Clustering algorithms; Computer science; Cybernetics; Dictionaries; Machine learning; Machine learning algorithms; Natural language processing; Supervised learning; Support vector machine classification; Support vector machines; Training data; Unsupervised learning; Support Vector Machine; Word Sense Disambiguation; WordNet; supervised learning; unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258922

Filename

4028513