Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Coreference Resolution by Leveraging Entity-Centric Features
  with Graph Neural Networks and Second-order Inference   One of the major challenges in coreference resolution is how to make use of
entity-level features defined over clusters of mentions rather than mention
pairs. However, coreferent mentions usually spread far apart in an entire text,
which makes it extremely difficult to incorporate entity-level features. We
propose a graph neural network-based coreference resolution method that can
capture the entity-centric information by encouraging the sharing of features
across all mentions that probably refer to the same real-world entity. Mentions
are linked to each other via the edges modeling how likely two linked mentions
point to the same entity. Modeling by such graphs, the features between
mentions can be shared by message passing operations in an entity-centric
manner. A global inference algorithm up to second-order features is also
presented to optimally cluster mentions into consistent groups. Experimental
results show our graph neural network-based method combing with the
second-order decoding algorithm (named GNNCR) achieved close to
state-of-the-art performance on the English CoNLL-2012 Shared Task dataset.

35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat