DocumentCode :
3289489
Title :
Measuring Taxonomic Similarity between Words Using Restrictive Context Matrices
Author :
Wang, Shi ; Cao, Cungen ; Cao, Ya-nan ; Lu, Han ; Cao, Xinyu
Author_Institution :
Key Lab. of Intell. Inf. Process., Chinese Acad. of Sci., Beijing
Volume :
4
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
193
Lastpage :
197
Abstract :
Measuring taxonomic similarity between words plays an important role in many semantic-based applications but still remains a challenging task today. We propose a new method which utilizes restrictive context matrices for this problem. We learn a set of special lexico-syntactic patterns automatically and use them to extract taxonomic related contexts of words from raw text. These restrictive contexts are then transformed into real matrices and similarities between them are calculated to reflect the taxonomic similarities between words. The main contribution of our work is that taxonomic related context of words can be mined, evaluated, and used to measure taxonomic similarities between words. Experimental results on Miller-Charles benchmark dataset achieve a correlation coefficient of 0.856.
Keywords :
matrix algebra; word processing; correlation coefficient; lexico-syntactic patterns; restrictive context matrices; taxonomic similarity; words; Fuzzy systems; Information processing; Information retrieval; Laboratories; Machine learning; Natural language processing; Ontologies; Robustness; Thesauri; Web search; restrictive context matrices; taxonomic similarity; text mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on
Conference_Location :
Jinan Shandong
Print_ISBN :
978-0-7695-3305-6
Type :
conf
DOI :
10.1109/FSKD.2008.236
Filename :
4666382
Link To Document :
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