Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Local Hypergraph-based Nested Named Entity Recognition as Query-based
  Sequence Labeling   There has been a growing academic interest in the recognition of nested named
entities in many domains. We tackle the task with a novel local
hypergraph-based method: We first propose start token candidates and generate
corresponding queries with their surrounding context, then use a query-based
sequence labeling module to form a local hypergraph for each candidate. An end
token estimator is used to correct the hypergraphs and get the final
predictions. Compared to span-based approaches, our method is free of the high
computation cost of span sampling and the risk of losing long entities.
Sequential prediction makes it easier to leverage information in word order
inside nested structures, and richer representations are built with a local
hypergraph. Experiments show that our proposed method outperforms all the
previous hypergraph-based and sequence labeling approaches with large margins
on all four nested datasets. It achieves a new state-of-the-art F1 score on the
ACE 2004 dataset and competitive F1 scores with previous state-of-the-art
methods on three other nested NER datasets: ACE 2005, GENIA, and KBP 2017.

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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph