Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finding Salient Context based on Semantic Matching for Relevance Ranking   In this paper, we propose a salient-context based semantic matching method to
improve relevance ranking in information retrieval. We first propose a new
notion of salient context and then define how to measure it. Then we show how
the most salient context can be located with a sliding window technique.
Finally, we use the semantic similarity between a query term and the most
salient context terms in a corpus of documents to rank those documents.
Experiments on various collections from TREC show the effectiveness of our
model compared to the state-of-the-art methods.

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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph