Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using WordNet to Complement Training Information in Text Categorization   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.

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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex