Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SEE: Syntax-aware Entity Embedding for Neural Relation Extraction   Distant supervised relation extraction is an efficient approach to scale
relation extraction to very large corpora, and has been widely used to find
novel relational facts from plain text. Recent studies on neural relation
extraction have shown great progress on this task via modeling the sentences in
low-dimensional spaces, but seldom considered syntax information to model the
entities. In this paper, we propose to learn syntax-aware entity embedding for
neural relation extraction. First, we encode the context of entities on a
dependency tree as sentence-level entity embedding based on tree-GRU. Then, we
utilize both intra-sentence and inter-sentence attentions to obtain sentence
set-level entity embedding over all sentences containing the focus entity pair.
Finally, we combine both sentence embedding and entity embedding for relation
classification. We conduct experiments on a widely used real-world dataset and
the experimental results show that our model can make full use of all
informative instances and achieve state-of-the-art performance of relation
extraction.

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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: stat
0
The categories of this paper are: hep-ph