Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature Selective Likelihood Ratio Estimator for Low- and Zero-frequency
  N-grams   In natural language processing (NLP), the likelihood ratios (LRs) of N-grams
are often estimated from the frequency information. However, a corpus contains
only a fraction of the possible N-grams, and most of them occur infrequently.
Hence, we desire an LR estimator for low- and zero-frequency N-grams. One way
to achieve this is to decompose the N-grams into discrete values, such as
letters and words, and take the product of the LRs for the values. However,
because this method deals with a large number of discrete values, the running
time and memory usage for estimation are problematic. Moreover, use of
unnecessary discrete values causes deterioration of the estimation accuracy.
Therefore, this paper proposes combining the aforementioned method with the
feature selection method used in document classification, and shows that our
estimator provides effective and efficient estimation results for low- and
zero-frequency N-grams.

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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
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The categories of this paper are: econ
5
The categories of this paper are: gr-qc
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The categories of this paper are: stat
7
The categories of this paper are: hep-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat