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        <title>Pyramid: A Layered Model for Nested Named Entity Recognition</title>
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    <abstract>This paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER). In our approach, token or text region embeddings are recursively inputted into L flat NER layers, from bottom to top, stacked in a pyramid shape. Each time an embedding passes through a layer of the pyramid, its length is reduced by one. Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention. We also design an inverse pyramid to allow bidirectional interaction between layers. The proposed method achieves state-of-the-art F1 scores in nested NER on ACE-2004, ACE-2005, GENIA, and NNE, which are 80.27, 79.42, 77.78, and 93.70 with conventional embeddings, and 87.74, 86.34, 79.31, and 94.68 with pre-trained contextualized embeddings. In addition, our model can be used for the more general task of Overlapping Named Entity Recognition. A preliminary experiment confirms the effectiveness of our method in overlapping NER.</abstract>
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%0 Conference Proceedings
%T Pyramid: A Layered Model for Nested Named Entity Recognition
%A Wang, Jue
%A Shou, Lidan
%A Chen, Ke
%A Chen, Gang
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-pyramid
%X This paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER). In our approach, token or text region embeddings are recursively inputted into L flat NER layers, from bottom to top, stacked in a pyramid shape. Each time an embedding passes through a layer of the pyramid, its length is reduced by one. Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention. We also design an inverse pyramid to allow bidirectional interaction between layers. The proposed method achieves state-of-the-art F1 scores in nested NER on ACE-2004, ACE-2005, GENIA, and NNE, which are 80.27, 79.42, 77.78, and 93.70 with conventional embeddings, and 87.74, 86.34, 79.31, and 94.68 with pre-trained contextualized embeddings. In addition, our model can be used for the more general task of Overlapping Named Entity Recognition. A preliminary experiment confirms the effectiveness of our method in overlapping NER.
%R 10.18653/v1/2020.acl-main.525
%U https://aclanthology.org/2020.acl-main.525
%U https://doi.org/10.18653/v1/2020.acl-main.525
%P 5918-5928

Markdown (Informal)

[Pyramid: A Layered Model for Nested Named Entity Recognition](https://aclanthology.org/2020.acl-main.525) (Wang et al., ACL 2020)

• Pyramid: A Layered Model for Nested Named Entity Recognition (Wang et al., ACL 2020)

ACL

• Jue Wang, Lidan Shou, Ke Chen, and Gang Chen. 2020. Pyramid: A Layered Model for Nested Named Entity Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5918–5928, Online. Association for Computational Linguistics.