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- W3141588745 abstract "Named Entity Recognition (NER) is generally regarded as a sequence labeling task, which faces a serious problem when the named entities are nested. In this paper, we propose a span-based model for nested NER, which enumerates all possible spans as potential entity mentions in a sentence and classifies them with pretrained BERT model. In view of the phenomenon that there are too many negative samples in all spans, we propose a multi-task learning method, which divides NER task into entity identification and entity classification task. In addition, we propose the entity IoU loss function to focus our model on the hard negative samples. We evaluate our model on three standard 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." @default.
- W3141588745 created "2021-04-13" @default.
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- W3141588745 date "2021-01-01" @default.
- W3141588745 modified "2023-10-16" @default.
- W3141588745 title "Span-Based Nested Named Entity Recognition with Pretrained Language Model" @default.
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- W3141588745 doi "https://doi.org/10.1007/978-3-030-73197-7_42" @default.
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