Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Substitute Based SCODE Word Embeddings in Supervised NLP Tasks   We analyze a word embedding method in supervised tasks. It maps words on a
sphere such that words co-occurring in similar contexts lie closely. The
similarity of contexts is measured by the distribution of substitutes that can
fill them. We compared word embeddings, including more recent representations,
in Named Entity Recognition (NER), Chunking, and Dependency Parsing. We examine
our framework in multilingual dependency parsing as well. The results show that
the proposed method achieves as good as or better results compared to the other
word embeddings in the tasks we investigate. It achieves state-of-the-art
results in multilingual dependency parsing. Word embeddings in 7 languages are
available for public use.

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The categories of this paper are: solv-int
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The categories of this paper are: math
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol