Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FAQ-based Question Answering via Word Alignment   In this paper, we propose a novel word-alignment-based method to solve the
FAQ-based question answering task. First, we employ a neural network model to
calculate question similarity, where the word alignment between two questions
is used for extracting features. Second, we design a bootstrap-based feature
extraction method to extract a small set of effective lexical features. Third,
we propose a learning-to-rank algorithm to train parameters more suitable for
the ranking tasks. Experimental results, conducted on three languages (English,
Spanish and Japanese), demonstrate that the question similarity model is more
effective than baseline systems, the sparse features bring 5% improvements on
top-1 accuracy, and the learning-to-rank algorithm works significantly better
than the traditional method. We further evaluate our method on the answer
sentence selection task. Our method outperforms all the previous systems on the
standard TREC data set.

27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
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The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th