Semantic enrichment of text representation with wikipedia for text classification
|Semantic enrichment of text representation with wikipedia for text classification|
|Author(s)||Yamakawa H., Peng J., Feldman A.|
|Published in||Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics|
|Keyword(s)||Ensemble, Semantics, Text classification, Text representation, Voting, Wikipedia (Extra: Ensemble, Text classification, Text representation, Voting, Wikipedia, Classifiers, Cybernetics, Information retrieval systems, Knowledge representation, Semantics, Text processing)|
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Semantic enrichment of text representation with wikipedia for text classification is a 2010 conference paper written in English by Yamakawa H., Peng J., Feldman A. and published in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics.
Text classification is a widely studied topic in the area of machine learning. A number of techniques have been developed to represent and classify text documents. Most of the techniques try to achieve good classification performance while taking a document only by its words (e.g. statistical analysis on word frequency and distribution patterns). One of the recent trends in text classification research is to incorporate more semantic interpretation in text classification, especially by using Wikipedia. This paper introduces a technique for incorporating the vast amount of human knowledge accumulated in Wikipedia into text representation and classification. The aim is to improve classification performance by transforming general terms into a set of related concepts grouped around semantic themes. In order to achieve this goal, this paper proposes a unique method for breaking the enormous amount of extracted Wikipedia knowledge (concepts) into smaller pieces (subsets of concepts). The subsets of concepts are separately used to represent the same set of documents in a number of different ways, from which an ensemble of classifiers is built. Experimental results show that an ensemble of classifiers individually trained on a different representation of the document set performs better with increased accuracy and stability than that of a classifier trained only on the original document set.
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