Preference learning for category-ranking based interactive text categorization

Fabio Aiolli, Fabrizio Sebastiani, Alessandro Sperduti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Category Ranking is a variant of the multi-label classification problem, in which, rather than performing a (hard) assignment to an object of categories from a predefined set, we rank all categories according to their estimated "degree of suitability" to the object. Category ranking has many applications, all pertaining to "interactive" classification contexts in which the system, rather than taking a final categorization decision, is simply required to support a human expert who is in charge of taking this decision. Despite its high applicative potential in information retrieval applications, and in text categorization in particular, category ranking has mainly been tackled by standard text categorization methods. In this paper, we take a radically different stand to category ranking, i.e. one in which supervision is provided to the learner not in the standard form of labels attached to training documents, but in the form of preferences of type "category c1 is to be preferred to category c2 for document d". We apply to this problem a recently proposed, very general model for preferential learning, and show, through experiments performed on the standard Reuters-21578 benchmark, that this largely outperforms support vector machines, the learning method which has up to now proved the best-performing one in text categorization comparative experiments.

Original languageEnglish
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages2034-2039
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2007
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: 12 Aug 200717 Aug 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

Conference2007 International Joint Conference on Neural Networks, IJCNN 2007
CountryUnited States
CityOrlando, FL
Period12/8/0717/8/07

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ASJC Scopus subject areas

  • Software

Cite this

Aiolli, F., Sebastiani, F., & Sperduti, A. (2007). Preference learning for category-ranking based interactive text categorization. In The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings (pp. 2034-2039). [4371271] (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/IJCNN.2007.4371271