Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Type Information to Improve Entity Coreference Resolution   Coreference resolution (CR) is an essential part of discourse analysis. Most
recently, neural approaches have been proposed to improve over SOTA models from
earlier paradigms. So far none of the published neural models leverage external
semantic knowledge such as type information. This paper offers the first such
model and evaluation, demonstrating modest gains in accuracy by introducing
either gold standard or predicted types. In the proposed approach, type
information serves both to (1) improve mention representation and (2) create a
soft type consistency check between coreference candidate mentions. Our
evaluation covers two different grain sizes of types over four different
benchmark corpora.

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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: econ
35
The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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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: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys