Document (#13269)

Author
Yang, Y.
Wilbur, J.
Title
Using corpus statistics to remove redundant words in text categorization
Source
Journal of the American Society for Information Science. 47(1996) no.5, S.357-369
Year
1996
Abstract
This article studies aggressive word removal in text categorization to reduce the noice in free texts to enhance the computational efficiency of categorization. We use a novel stop word identification method to automatically generate domain specific stoplists which are much larger than a conventional domain-independent stoplist. In our tests with 3 categorization methods on text collections from different domains/applications, significant numbers of words were removed without sacrificing categorization effectiveness. In the test of the Expert Network method on CACM documents, for example, an 87% removal of unique qords reduced the vocabulary of documents from 8.002 distinct words to 1.045 words, which resulted in a 63% time savings and a 74% memory savings in the computation of category ranking, with a 10% precision improvement on average over not using word removal. It is evident in this study that automated word removal based on corpus statistics has a practical and significant impact on the computational tractability of categorization methods in large databases
Theme
Computerlinguistik

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  5. Schutze, H.; Pederson, J.O.: ¬A cooccurrence-based thesaurus and two applications to information retrieval (1997) 0.15
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