An Embarrassingly Easy but Strong Baseline for Nested Named Entity
Recognition
- URL: http://arxiv.org/abs/2208.04534v1
- Date: Tue, 9 Aug 2022 04:33:46 GMT
- Title: An Embarrassingly Easy but Strong Baseline for Nested Named Entity
Recognition
- Authors: Hang Yan, Yu Sun, Xiaonan Li, Xipeng Qiu
- Abstract summary: We propose using Conal Neural Network (CNN) to model spatial relations in the score matrix.
Our model surpasses several recently proposed methods with the same pre-trained encoders.
- Score: 55.080101447586635
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Named entity recognition (NER) is the task to detect and classify the entity
spans in the text. When entity spans overlap between each other, this problem
is named as nested NER. Span-based methods have been widely used to tackle the
nested NER. Most of these methods will get a score $n \times n$ matrix, where
$n$ means the length of sentence, and each entry corresponds to a span.
However, previous work ignores spatial relations in the score matrix. In this
paper, we propose using Convolutional Neural Network (CNN) to model these
spatial relations in the score matrix. Despite being simple, experiments in
three commonly used nested NER datasets show that our model surpasses several
recently proposed methods with the same pre-trained encoders. Further analysis
shows that using CNN can help the model find nested entities more accurately.
Besides, we found that different papers used different sentence tokenizations
for the three nested NER datasets, which will influence the comparison. Thus,
we release a pre-processing script to facilitate future comparison.
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