Automatic Generation of Hierarchical Taxonomies for Text Categorization

Although considerable research has been conducted in the field of hierarchical text categorization, little has been done on automatically collecting labeled corpus for building hierarchical taxonomies. In this paper, we propose an automatic method of collecting training samples for building hierarchical taxonomies. In our method, the category node is initially defined by some keywords, the web search engine is then used to construct a small set of labeled documents, and topic tracking algorithm based on document length normalization is applied to enlarge the training corpus on the bases of the seed documents. We also design a method to check the consistency of the collected corpus. The above steps produces a flat category structure which contains all the categories for building the hierarchical taxonomy. Next, linear discriminant projection approach is utilized to construct more meaningful intermediate levels of hierarchies in the generated flat set of categories. Experimental results show that training corpus is good enough for statistical classification methods.

By: Li Zhang; Tao Li; Shi Xia Liu; Yue Pan

Published in: RC23517 in 2005


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