Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An
  Exemplar-Based Approach   In this paper, we present a new approach for word sense disambiguation (WSD)
using an exemplar-based learning algorithm. This approach integrates a diverse
set of knowledge sources to disambiguate word sense, including part of speech
of neighboring words, morphological form, the unordered set of surrounding
words, local collocations, and verb-object syntactic relation. We tested our
WSD program, named {\sc Lexas}, on both a common data set used in previous
work, as well as on a large sense-tagged corpus that we separately constructed.
{\sc Lexas} achieves a higher accuracy on the common data set, and performs
better than the most frequent heuristic on the highly ambiguous words in the
large corpus tagged with the refined senses of {\sc WordNet}.

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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci