Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named
  Entity Recognition   Nested named entity recognition (nested NER) is a fundamental task in natural
language processing. Various span-based methods have been proposed to detect
nested entities with span representations. However, span-based methods do not
consider the relationship between a span and other entities or phrases, which
is helpful in the NER task. Besides, span-based methods have trouble predicting
long entities due to limited span enumeration length. To mitigate these issues,
we present the Propose-and-Refine Network (PnRNet), a two-stage set prediction
network for nested NER. In the propose stage, we use a span-based predictor to
generate some coarse entity predictions as entity proposals. In the refine
stage, proposals interact with each other, and richer contextual information is
incorporated into the proposal representations. The refined proposal
representations are used to re-predict entity boundaries and classes. In this
way, errors in coarse proposals can be eliminated, and the boundary prediction
is no longer constrained by the span enumeration length limitation.
Additionally, we build multi-scale sentence representations, which better model
the hierarchical structure of sentences and provide richer contextual
information than token-level representations. Experiments show that PnRNet
achieves state-of-the-art performance on four nested NER datasets and one flat
NER dataset.

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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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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: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: physics
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg