Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Application of Fuzzy Logic to Collocation Extraction   Collocations are important for many tasks of Natural language processing such
as information retrieval, machine translation, computational lexicography etc.
So far many statistical methods have been used for collocation extraction.
Almost all the methods form a classical crisp set of collocation. We propose a
fuzzy logic approach of collocation extraction to form a fuzzy set of
collocations in which each word combination has a certain grade of membership
for being collocation. Fuzzy logic provides an easy way to express natural
language into fuzzy logic rules. Two existing methods; Mutual information and
t-test have been utilized for the input of the fuzzy inference system. The
resulting membership function could be easily seen and demonstrated. To show
the utility of the fuzzy logic some word pairs have been examined as an
example. The working data has been based on a corpus of about one million words
contained in different novels constituting project Gutenberg available on
www.gutenberg.org. The proposed method has all the advantages of the two
methods, while overcoming their drawbacks. Hence it provides a better result
than the two methods.

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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: eess
0
The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int