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- W2293570855 abstract "Within text categorization and other data mining tasks, the use of suitable methods for term weighting canbring a substantial boost in effectiveness. Several term weighting methods have been presented throughoutliterature, based on assumptions commonly derived from observation of distribution of words in documents.For example, the idf assumption states that words appearing in many documents are usually not as importantas less frequent ones. Contrarily to tf.idf and other weighting methods derived from information retrieval,schemes proposed more recently are supervised, i.e. based on knownledge of membership of training documentsto categories. We propose here a supervised variant of the tf.idf scheme, based on computing theusual idf factor without considering documents of the category to be recognized, so that importance of termsfrequently appearing only within it is not underestimated. A further proposed variant is additionally based onrelevance frequency, considering occurrences of words within the category itself. In extensive experiments ontwo recurring text collections with several unsupervised and supervised weighting schemes, we show that theones we propose generally perform better than or comparably to other ones in terms of accuracy, using twodifferent learning methods." @default.
- W2293570855 created "2016-06-24" @default.
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- W2293570855 date "2015-01-01" @default.
- W2293570855 modified "2023-09-26" @default.
- W2293570855 title "A Study on Term Weighting for Text Categorization: A Novel Supervised Variant of tf.idf" @default.
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- W2293570855 doi "https://doi.org/10.5220/0005511900260037" @default.
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