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Planarized sentence representation for nested named entity recognition
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DOI：10.1016/j.ipm.2023.103352.png)
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摘要
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One strategy to recognize nested entities is to enumerate overlapped entity spans for classi-fication. However, current models independently verify every entity span, which ignores the semantic dependency between spans. In this paper, we first propose a planarized sentence representation to represent nested named entities. Then, a bi-directional two-dimensional recurrent operation is implemented to learn semantic dependencies between spans. Our method is evaluated on seven public datasets for named entity recognition. It achieves competitive performance in named entity recognition. The experimental results show that our method is effective to resolve nested named entities and learn semantic dependencies between them.
Keyword:
Named entity recognition
Sentence representation
Self-cross encoding
Planarized sentence representation
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6.9
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5.2K
被引数:
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Handling negative samples problems in span-based nested named entity recognition.png)
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