Abstract
Automatic text categorization is still a very important research topic. Typical applications include assisting end-users in archiving existing documents, or helping them in browsing existing corpus of documents in a hierarchical way. Text categorization is usually composed of two main steps: keyword extraction and classification. In this paper, a corpus of documents is represented by a binary relation linking each document to the words it contains. From this relation, the Hyper Rectangle Algorithm extracts the list of the most representative words in a hierarchical way. A hyper-Rectangle associated to an element of the range of a binary relation is the union of all non-enlargeable rectangles containing it. The extracted keywords are fed into the random forest classifier in order to predict the category of each document. The method has been validated on the popular Reuters 21578 news articles database. Results are very promising and show the effectiveness of the Hyper Rectangular method in extracting relevant keywords.
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Hassaine, A., Mecheter, S., Jaoua, A. (2015). Text Categorization Using Hyper Rectangular Keyword Extraction: Application to News Articles Classification. In: Kahl, W., Winter, M., Oliveira, J. (eds) Relational and Algebraic Methods in Computer Science. RAMICS 2015. Lecture Notes in Computer Science(), vol 9348. Springer, Cham. https://doi.org/10.1007/978-3-319-24704-5_19
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