The Role of Parts-of-Speech in Feature Selection
نویسنده
چکیده
This research explores the role of parts-of-speech (POS) in feature selection in text categorization. We compare the use of different POS, namely nouns, verbs, adjectives and adverbs with a feature set that contains all POS. The best results are obtained with the use of only nouns. Therefore, we make use of a WordNet-based POS feature selection approach using the nouns feature set to compare with popular feature selection methods, namely Chi Square (Chi2) and Information Gain (IG). We find that the WordNet-based POS approach using only nouns as features can outperform Chi2 and IG in categorization effectiveness. Here, a machine learning approach to text categorization is employed and the Reuters-21578 top ten categories are used as the dataset.
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