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by Marti A. Hearst, Hinrich Schutze
Proc. of the Workshop on Extracting Lexical Knowledge
http://www.sims.berkeley.edu/~hearst/papers/siglex93.ps.gz
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Abstract:
We discuss a method for augmenting and rearranging a structured lexicon in order to make it more suitable for a topic labeling task, by making use of lexical association information from a large text corpus. We first describe an algorithm for converting the hierarchical structure of WordNet [13] into a set of flat categories. We then use lexical cooccurrence statistics in combination with these categories to classify proper names, assign more specific senses to broadly defined terms, and classify new words into existing categories. We also describe how to use these statistics to assign schema-like information to the categories and show how the new categories improve a text-labeling algorithm. In effect, we provide a mechanism for successfully combining
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