Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Introducing Syntactic Structures into Target Opinion Word Extraction
  with Deep Learning   Targeted opinion word extraction (TOWE) is a sub-task of aspect based
sentiment analysis (ABSA) which aims to find the opinion words for a given
aspect-term in a sentence. Despite their success for TOWE, the current deep
learning models fail to exploit the syntactic information of the sentences that
have been proved to be useful for TOWE in the prior research. In this work, we
propose to incorporate the syntactic structures of the sentences into the deep
learning models for TOWE, leveraging the syntax-based opinion possibility
scores and the syntactic connections between the words. We also introduce a
novel regularization technique to improve the performance of the deep learning
models based on the representation distinctions between the words in TOWE. The
proposed model is extensively analyzed and achieves the state-of-the-art
performance on four benchmark datasets.

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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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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: math