Computer Science – Computation and Language
Scientific paper
1997-09-17
Second International Conference on Recent Advances in Natural Language Processing, 1997
Computer Science
Computation and Language
16 pages, 1 figure, 3 tables, previously with RANLP latext style
Scientific paper
Automatic Text Categorization (TC) is a complex and useful task for many natural language applications, and is usually performed through the use of a set of manually classified documents, a training collection. We suggest the utilization of additional resources like lexical databases to increase the amount of information that TC systems make use of, and thus, to improve their performance. Our approach integrates WordNet information with two training approaches through the Vector Space Model. The training approaches we test are the Rocchio (relevance feedback) and the Widrow-Hoff (machine learning) algorithms. Results obtained from evaluation show that the integration of WordNet clearly outperforms training approaches, and that an integrated technique can effectively address the classification of low frequency categories.
Agudo Belen Diaz
Buenaga Rodriguez Manuel de
Gomez Hidalgo Jose Maria
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