Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Hierarchical Entity Graph Convolutional Network for Relation
  Extraction across Documents   Distantly supervised datasets for relation extraction mostly focus on
sentence-level extraction, and they cover very few relations. In this work, we
propose cross-document relation extraction, where the two entities of a
relation tuple appear in two different documents that are connected via a chain
of common entities. Following this idea, we create a dataset for two-hop
relation extraction, where each chain contains exactly two documents. Our
proposed dataset covers a higher number of relations than the publicly
available sentence-level datasets. We also propose a hierarchical entity graph
convolutional network (HEGCN) model for this task that improves performance by
1.1\% F1 score on our two-hop relation extraction dataset, compared to some
strong neural baselines.

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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga