Integrating A Lexical Database And A Training Collection For Text Categorization

Automatic text categorization is a complex and useful task for manynatural language processing applications. Recent approaches to textcategorization focus more on algorithms than on resources involved in thisoperation. In contrast to this trend, we present an approach based on the integration of widely available resources aslexical databases and training collections to overcome current limitationsof the task. Our approach ~ makes use of WordNet synonymy information toincrease evidence for bad trained categories. When testing a direct categorization, a WordNet basedone, a training algorithm, and our integrated approach, the latter exhibitsa better perfomance than any of the others. Incidentally, WordNet based approach perfomance is comparable with the trainingapproach one. 1 I n t r o d u c t i o n Text categorization (TC) is the classification ofdocuments with respect to a set of one or more pre-existing categories. TCis a hard and very useful operation frequently applied to the assignment of subject categories to documents, toroute and filter texts, or as a part of natural language processingsystems. In this paper we present an automatic TC approach based on theuse of several linguistic resources. Nowadays, many resources like trainingcollections and lexical databases have been successfully employed for text classificationtasks [Boguraev and Pustejovsky, 1996], but always in an isolated way. Thecurrent trend in the TC field is to pay more attention to algorithms thanto resources. We believe that the key idea for the improvement of text categorization is increasing theamount of information a system makes use of, through the integration ofseveral resources. We have chosen the Information Retrieval vector space model for ourapproach. Term weight vectors are computed for documents and categoriesemploying the lexical database WordNet and the training subset of the testcollection Reuters-22173. We calculate the weight vectors for: 1 This research is supported by the Spanish Commttee of Sctence andTechnology (CICYT TIC94-0187). _ A direct approach, _ a Wordnet based approach, _ a training collection approach, _ and finally, a technique for integrating WordNet and a training collection. Later, we compare document-category similarity by means of a cosine-basedfunction. We have driven a series of experiments on the test subset of Reuters22173, which yields two conclusions. First, the integrated approach performs better than any of the other ones, confirming thehypothesis that the more informed a text classification system is, thebetter it performs. Secondly, the lexical database oriented technique can rival with the training approach, avoiding the necessity ofcost-expensive building of training collections for any domain andclassification task. 2 T a s k D e s c r i p t i o n Given a set of documents and a set of categories, the goal of acategorization system is to decide whether any document belongs to anycategory or not. The system makes use of the information contained in adocument to compute a degree of pertainance of the document to each category. Categories are usually subject labels likeart or military, but other categories like text genres are also interesting[Karlgren and Cutting, 1994]. Documents can be news stories, emailmessages, reports, and so forth. The most widely used resource for TC is the training collection. Attaining collection is a set of manually classified documents that allowsthe system to guess clues on how to classify new unseen documents. Thereare currently several TC test collections, from which a training subset and a test subset can be obtained. Forinstance, the huge TREC collection [Harman, 1996], OHSUMED [Hersh etal, 1994] and Reuters-22173 [Lewis, 1992] have been collected for thistask. We have selected Reuters because it has been used in other work,facilitating the comparison of resuits. Lexical databases have been rarely employed in TC, but severalapproaches have demonstrated their usefulness for term classification operations like word sense disambiguation[Resnik, 1995; Agirre and Rigau, 1996]. A lexical database is a referencesystem that accumulates information on the lexical items of one o