An Automatic Clustering of Articles Using Dictionary Definitions

An Automatic Clustering of Articles Using Dictionary Definitions Fumiyo FUKUMOTO Yoshimi SUZUKI~ Dept. of Electrical Engineering a,nd Computer Science, Yalnanashi University 4-3-11 T~keda, Kofu 4(10 ,b~pan {fukumotoOskye, ysuzuki~suwaj } . esi. yamanashi, ac. jp Abstract In this paper, we propose a statistical approach for clustering of artMes us- ing on-line dictionary definitions. One of the characteristics of our approach is that every sense of word in artMes is au- tomatically disambiguated using dictio- nary definitions. The other is that in or- der to cope with the problem of a phrasal lexicon, linking which links words with their semantically similar words in arti- cles is introduced in our method. The results of experiments demonstrate the effectiveness of the proposed method. 1 Introduction There has been quite a lot of research con- cerned with automatic clustering of articles or autonmtic identification of selnantically similar articles(Walker, 1986), (Guthrie, 1994), (Yuasa, 1995). Most of these works deal with entirely dif ferent articles. In general, the 1)rot)l(m: that the same word ca:, be used differently in different sul)jeet domains is less problematic in entirely ditferent artMes, such as 'weather forecasts', 'medical rel)orts' , and 'com- puter manuals'. Because these articles are charac- terised by a larger number of different words than that of the same words. However, in texts from a restricted domain such as financial artMes, e.g Wall Street Journal (WS,I in short) (Libernmn, 1990), one encounters quite a large number of pop ysemous words. Therefore, polyseinous words of_ ten hamper the I)recise cla~ssification of artMes, each of which belongs to the restricted subject do- nlaiII. In this paper, we report an experimental study for clustering of articles by using on-line dic- tionary definitions attd show how dictionary- definition can use effectively to classify articles, each of which belongs to the restricted subject do- main. We first describe a method for disambiguat- ing word-senses in articles based on dictionary def- initions. Then, we present a method for classifying articles and finally, we rel)ort some ext)eriments in order to show the effect of the method. 2 Related Work One of major approaches in automatic clustering of articles is based on statistical information of words in ,~rticles. Every article is characterised by a vector, each dimension of which is associated with a specific word in articles, and every coordi- nate of the artMe is represented by tern: weight- ing. Tern1 weighting methods have been widely studied in iufornmtion retrieval research (Salton, 1983), (Jones, 1972) and some of then: are used in an automatic clustering of articles. Guthrie and Yuasa used word frequencies for weighting (Guthrie, 1994), (Yuasa, 1995), and Tokunaga used weighted inverse document frequency which is a word frequency within the document divided by its fl'equency throughout the entire document collection (Tokunaga, 1994). The results of these methods when al)plied to articles' cbussification task, seem to show its etfectiveness. However, these works do not seriously deal with the 1)roblem of polysemy. The alternative al)l)roach is based on dictio- nary's infl)rlnation as a thesaurus. One of …

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