Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Knowledge-Driven Approach to Classifying Object and Attribute
  Coreferences in Opinion Mining   Classifying and resolving coreferences of objects (e.g., product names) and
attributes (e.g., product aspects) in opinionated reviews is crucial for
improving the opinion mining performance. However, the task is challenging as
one often needs to consider domain-specific knowledge (e.g., iPad is a tablet
and has aspect resolution) to identify coreferences in opinionated reviews.
Also, compiling a handcrafted and curated domain-specific knowledge base for
each domain is very time consuming and arduous. This paper proposes an approach
to automatically mine and leverage domain-specific knowledge for classifying
objects and attribute coreferences. The approach extracts domain-specific
knowledge from unlabeled review data and trains a knowledgeaware neural
coreference classification model to leverage (useful) domain knowledge together
with general commonsense knowledge for the task. Experimental evaluation on
realworld datasets involving five domains (product types) shows the
effectiveness of the approach.

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The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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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: hep-ex
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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: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc