Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mention-centered Graph Neural Network for Document-level Relation
  Extraction   Document-level relation extraction aims to discover relations between
entities across a whole document. How to build the dependency of entities from
different sentences in a document remains to be a great challenge. Current
approaches either leverage syntactic trees to construct document-level graphs
or aggregate inference information from different sentences. In this paper, we
build cross-sentence dependencies by inferring compositional relations between
inter-sentence mentions. Adopting aggressive linking strategy, intermediate
relations are reasoned on the document-level graphs by mention convolution. We
further notice the generalization problem of NA instances, which is caused by
incomplete annotation and worsened by fully-connected mention pairs. An
improved ranking loss is proposed to attend this problem. Experiments show the
connections between different mentions are crucial to document-level relation
extraction, which enables the model to extract more meaningful higher-level
compositional relations.

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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
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The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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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: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas