Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Hybrid Approach to Extract Keyphrases from Medical Documents   Keyphrases are the phrases, consisting of one or more words, representing the
important concepts in the articles. Keyphrases are useful for a variety of
tasks such as text summarization, automatic indexing,
clustering/classification, text mining etc. This paper presents a hybrid
approach to keyphrase extraction from medical documents. The keyphrase
extraction approach presented in this paper is an amalgamation of two methods:
the first one assigns weights to candidate keyphrases based on an effective
combination of features such as position, term frequency, inverse document
frequency and the second one assign weights to candidate keyphrases using some
knowledge about their similarities to the structure and characteristics of
keyphrases available in the memory (stored list of keyphrases). An efficient
candidate keyphrase identification method as the first component of the
proposed keyphrase extraction system has also been introduced in this paper.
The experimental results show that the proposed hybrid approach performs better
than some state-of-the art keyphrase extraction approaches.

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The categories of this paper are: econ
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg