A Comparison of Manual and Automatic Constructions of Category Hierarchy for Classifying Large Corpora

A Comparison of Manual and Automatic Constructions of Category Hierarchy for Classifying Large Corpora Fumiyo Fukumoto Interdisciplinary Graduate School of Medicine and Engineering Univ. of Yamanashi fukumoto@skye.esb.yamanashi.ac.jp Yoshimi Suzuki Interdisciplinary Graduate School of Medicine and Engineering Univ. of Yamanashi ysuzuki@ccn.yamanashi.ac.jp Abstract We address the problem dealing with a large collection of data, and investigate the use of automatically constructing category hierarchy from a given set of categories to improve clas- sification of large corpora. We use two well- known techniques, partitioning clustering, �- means and a � ��� � ������� to create category hierarchy. �-means is to cluster the given cate- gories in a hierarchy. To select the proper num- ber of �, we use a � ��� � ������� which mea- sures the degree of our disappointment in any differences between the true distribution over inputs and the learner’s prediction. Once the optimal number of � is selected, for each clus- ter, the procedure is repeated. Our evaluation using the 1996 Reuters corpus which consists of 806,791 documents shows that automati- cally constructing hierarchy improves classifi- cation accuracy. 1 Introduction Text classification has an important role to play, espe- cially with the recent explosion of readily available on- line documents. Much of the previous work on text clas- sification use statistical and machine learning techniques. However, the increasing number of documents and cate- gories often hamper the development of practical classifi- cation systems, mainly by statistical, computational, and representational problems(Dietterich, 2000). One strat- egy for solving these problems is to use category hierar- chies. The idea behind this is that when humans organize extensive data sets into fine-grained categories, category hierarchies are often employed to make the large collec- tion of categories more manageable. McCallum et. al. presented a method called ‘shrink- age’ to improve parameter estimates by taking advan- tage of the hierarchy(McCallum, 1999). They tested their method using three different real-world datasets: 20,000 articles from the UseNet, 6,440 web pages from the In- dustry Sector, and 14,831 pages from the Yahoo, and showed improved performance. Dumais et. al. also de- scribed a method for hierarchical classification of Web content consisting of 50,078 Web pages for training, and 10,024 for testing, with promising results(Dumais and Chen, 2000). Both of them use hierarchies which are manually constructed. Such hierarchies are costly human intervention, since the number of categories and the size of the target corpora are usually very large. Further, man- ually constructed hierarchies are very general in order to meet the needs of a large number of forthcoming acces- sible source of text data, and sometimes constructed by relying on human intuition. Therefore, it is difficult to keep consistency, and thus, problematic for classifying text automatically. In this paper, we address the problem dealing with a large collection of data, and propose a method to gener- ate category hierarchy for text classification. Our method uses two well-known techniques, partitioning clustering method called �-means and a � ��� � ������� to create hierarchical structure. �-means partitions a set of …

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