Abstract

Text Classification classifies a document, under a predefined category. Mostly, an automatic text classification is an important application taken as a research topic, since the inception of digital documents. In this study, Hypernyms, superordinate words are identified in web and clubbed with entailment rule acquisition. Available tree of hyponym words in the document has been created and used with dependency tree. Features extraction is performed with weighted Term Frequency-Inverse Document Frequency (TF-IDF) where the weight of the word can be computed based on the number of hyponyms present in the radix tree. Performance evaluation is done using Support Vector Machine (SVM) classifier and Fuzzy Unordered Rule Induction Algorithm (FURIA) classifier.

Keywords:

Hypernym , hyponym, radix tree, Support Vector Machine (SVM) and Fuzzy Unordered Rule Induction Algorithm (FURIA), Term Frequency-Inverse Document Frequency (TF-IDF),

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