Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-distributional Word Vector Representations   Data-driven representation learning for words is a technique of central
importance in NLP. While indisputably useful as a source of features in
downstream tasks, such vectors tend to consist of uninterpretable components
whose relationship to the categories of traditional lexical semantic theories
is tenuous at best. We present a method for constructing interpretable word
vectors from hand-crafted linguistic resources like WordNet, FrameNet etc.
These vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We
analyze their performance on state-of-the-art evaluation methods for
distributional models of word vectors and find they are competitive to standard
distributional approaches.

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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: stat
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int