Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PoD: Positional Dependency-Based Word Embedding for Aspect Term
  Extraction   Dependency context-based word embedding jointly learns the representations of
word and dependency context, and has been proved effective in aspect term
extraction. In this paper, we design the positional dependency-based word
embedding (PoD) which considers both dependency context and positional context
for aspect term extraction. Specifically, the positional context is modeled via
relative position encoding. Besides, we enhance the dependency context by
integrating more lexical information (e.g., POS tags) along dependency paths.
Experiments on SemEval 2014/2015/2016 datasets show that our approach
outperforms other embedding methods in aspect term extraction.

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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: q-alg
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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: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th