Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LMN at SemEval-2022 Task 11: A Transformer-based System for English
  Named Entity Recognition   Processing complex and ambiguous named entities is a challenging research
problem, but it has not received sufficient attention from the natural language
processing community. In this short paper, we present our participation in the
English track of SemEval-2022 Task 11: Multilingual Complex Named Entity
Recognition. Inspired by the recent advances in pretrained Transformer language
models, we propose a simple yet effective Transformer-based baseline for the
task. Despite its simplicity, our proposed approach shows competitive results
in the leaderboard as we ranked 12 over 30 teams. Our system achieved a macro
F1 score of 72.50% on the held-out test set. We have also explored a data
augmentation approach using entity linking. While the approach does not improve
the final performance, we also discuss it in this paper.

35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat