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Few-shot nested named entity recognition
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AI
DOI：10.1016/j.knosys.2024.111688.png)
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摘要
En 中文
While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data has been challenging, especially for entities with nested structures commonly existing in NER datasets. Unlike flat entities, entities and their nested entities are more likely to have similar semantic feature representations, drastically increasing difficulties in classifying different entity categories. This paper posits that the few -shot nested NER task warrants its own dedicated attention and proposes a Global-Biaffine Positive -Enhanced (GBPE) framework for this new task. Within the GBPE framework, we first develop the new Global-Biaffine span representation to capture the span global dependency information for each entity span to distinguish nested entities. We then formulate a unique positive -enhanced contrastive loss function to enhance the utility of specific positive samples in contrastive learning for larger margins. Lastly, by using these enlarged margins, we obtain better margin constraints and incorporate them into the nearest neighbor inference to predict the unlabeled entities. Extensive experiments on three nested NER datasets in English, German, and Russian show that GBPE outperforms baseline models on the 1 -shot and 5 -shot tasks in terms of F 1 score.
Keyword:
Few-shot
Nested named entity recognition
Positive-enhanced contrastive loss
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期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
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分享收藏分享收藏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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