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Improving Text Classification by Using Conceptual and Contextual Features

The exponential growth of text available on the Internet has created a critical need for accurate, fast, and generalpurpose text classification algorithms. This paper examines the improvement of broadly based text classification by using features that are easily extracted from training documents. These features represent both the conceptual and contextual properties of a target class, and include synonyms, hypernyms, term frequency, and bigrams of nouns, synonyms and hypernyms. Multiple permutations of the features are applied to three different classification models (Coordinate matching, TF*IDF, and naive Bayes) over three diverse data sets (Reuters, USENET, and folk songs). The findings are also compared to previously published results for the rule-based learner Ripper and results obtained by using another naive Bayes classifier, Rainbow. Suggestions are made about how to automatically determine which features to use, based upon the data set in question. The results demonstrate that the introduction of both conceptual and contextual features decreases the error by as much as 33%.