Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Dynamic Context Augmentation for Global Entity Linking   Despite of the recent success of collective entity linking (EL) methods,
these "global" inference methods may yield sub-optimal results when the
"all-mention coherence" assumption breaks, and often suffer from high
computational cost at the inference stage, due to the complex search space. In
this paper, we propose a simple yet effective solution, called Dynamic Context
Augmentation (DCA), for collective EL, which requires only one pass through the
mentions in a document. DCA sequentially accumulates context information to
make efficient, collective inference, and can cope with different local EL
models as a plug-and-enhance module. We explore both supervised and
reinforcement learning strategies for learning the DCA model. Extensive
experiments show the effectiveness of our model with different learning
settings, base models, decision orders and attention mechanisms.

3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math