Within text categorization and other data mining tasks, the use of suitable methods for term weighting can bring a substantial boost in effectiveness. Several term weighting methods have been presented throughout literature, 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 important as 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 documents to categories. We propose here a supervised variant of the tf.idf scheme, based on computing the usual idf factor without considering documents of the category to be recognized, so that importance of terms frequently appearing only within it is not underestimated. A further proposed variant is additionally based on relevance frequency, considering occurrences of words within the category itself. In extensive experiments on two recurring text collections with several unsupervised and supervised weighting schemes, we show that the ones we propose generally perform better than or comparably to other ones in terms of accuracy, using two different learning methods.

A study on term weighting for text categorization: A novel supervised variant of tf.idf

DOMENICONI, GIACOMO;MORO, GIANLUCA;PASOLINI, ROBERTO;SARTORI, CLAUDIO
2015

Abstract

Within text categorization and other data mining tasks, the use of suitable methods for term weighting can bring a substantial boost in effectiveness. Several term weighting methods have been presented throughout literature, 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 important as 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 documents to categories. We propose here a supervised variant of the tf.idf scheme, based on computing the usual idf factor without considering documents of the category to be recognized, so that importance of terms frequently appearing only within it is not underestimated. A further proposed variant is additionally based on relevance frequency, considering occurrences of words within the category itself. In extensive experiments on two recurring text collections with several unsupervised and supervised weighting schemes, we show that the ones we propose generally perform better than or comparably to other ones in terms of accuracy, using two different learning methods.
DATA 2015 - 4th International Conference on Data Management Technologies and Applications, Proceedings
26
37
Domeniconi, Giacomo; Moro, Gianluca; Pasolini, Roberto; Sartori, Claudio
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/545299
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 34
  • ???jsp.display-item.citation.isi??? ND
social impact