Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A data-driven strategy to combine word embeddings in information
  retrieval   Word embeddings are vital descriptors of words in unigram representations of
documents for many tasks in natural language processing and information
retrieval. The representation of queries has been one of the most critical
challenges in this area because it consists of a few terms and has little
descriptive capacity. Strategies such as average word embeddings can enrich the
queries' descriptive capacity since they favor the identification of related
terms from the continuous vector representations that characterize these
approaches. We propose a data-driven strategy to combine word embeddings. We
use Idf combinations of embeddings to represent queries, showing that these
representations outperform the average word embeddings recently proposed in the
literature. Experimental results on benchmark data show that our proposal
performs well, suggesting that data-driven combinations of word embeddings are
a promising line of research in ad-hoc information retrieval.

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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: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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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: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas