A Classifier-based Text Mining Approach for Evaluating Semantic Relatedness Using Support Vector Machines

Authors:
Chung-Hong Lee;Hsin-Chang Yang
Affiliations:
National Kaohsiung University of Applied Sciences, Taiwan;Chang Jung University, Taiwan
Venue:
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
Year:
2005

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Abstract

The quantification of evaluating semantic relatedness among texts has been a challenging issue that pervades much of machine learning and natural language processing. This paper presents a hybrid approach of a text-mining technique for measuring semantic relatedness among texts. In this work we develop several text classifiers using Support Vector Machines (SVM) method to supporting acquisition of relatedness among texts. First, we utilized our developed text mining algorithms, including text mining techniques based on classification of texts in several text collections. After that, we employ various SVM classifiers to deal with evaluation of relatedness of the target documents. The results indicate that this approach can also be fitted to other research work, such as information filtering, and re-categorizing resulting documents of search engine queries.