A study on automatically extracted keywords in text categorization

Abstract

This paper presents a study on if and how automatically extracted keywords can be used to improve text categorization. In summary we show that a higher performance -as measured by micro-averaged F-measure on a standard text categorization collection -is achieved when the full-text representation is combined with the automatically extracted keywords. The combination is obtained by giving higher weights to words in the full-texts that are also extracted as keywords. We also present results for experiments in which the keywords are the only input to the categorizer, either represented as unigrams or intact. Of these two experiments, the unigrams have the best performance, although neither performs as well as headlines only.

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Automatic classification of sentences for evidence based medicine

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