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Handling negative samples problems in span-based nested named entity recognition
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DOI：10.1016/j.neucom.2022.07.012.png)
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摘要
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Named Entity Recognition (NER) is generally regarded as a sequence labeling task, and faces a serious problem when the named entities are nested. Span-based model, which enumerates all possible spans as potential entity mentions in a sentence and classifies them, is straightforward for nested NER but faces negative samples problems. In this paper, we propose a span-based nested NER model with BERT and try to solve the negative samples problems. In view of the phenomenon that there are too many negative samples in all spans, we employ a multi-task learning method, which divides NER task into entity iden-tification and entity classification task. In addition, we propose the entity IoU loss function to focus our model on the hard negative samples. Our model is evaluated on three nested NER datasets: GENIA, ACE2004 and ACE2005, and the results show that our model outperforms other state-of-the-art models with the same pretrained language model, achieving 79.46%, 87.30% and 85.24% respectively in terms of F1 score.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Natural language processing
Nested named entity recognition
Span-based model
Multi-task learning
Negative samples
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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引用论文
Hypergraph network model for nested entity mention recognition嵌套实体提及识别的超图网络模型.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Nested Named Entity Recognition via Second-best Sequence Learning and Decoding通过次优序列学习和解码进行嵌套命名实体识别
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