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        <title>Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks</title>
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        <namePart type="given">Songlin</namePart>
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    <abstract>Constituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans. In this work, we cast nested NER to constituency parsing and propose a novel pointing mechanism for bottom-up parsing to tackle both tasks. The key idea is based on the observation that if we traverse a constituency tree in post-order, i.e., visiting a parent after its children, then two consecutively visited spans would share a boundary. Our model tracks the shared boundaries and predicts the next boundary at each step by leveraging a pointer network. As a result, it needs only linear steps to parse and thus is efficient. It also maintains a parsing configuration for structural consistency, i.e., always outputting valid trees. Experimentally, our model achieves the state-of-the-art performance on PTB among all BERT-based models (96.01 F1 score) and competitive performance on CTB7 in constituency parsing; and it also achieves strong performance on three benchmark datasets of nested NER: ACE2004, ACE2005, and GENIA. Our code will be available at https://github.com/xxxxx.</abstract>
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    <identifier type="doi">10.18653/v1/2022.acl-long.171</identifier>
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%0 Conference Proceedings
%T Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks
%A Yang, Songlin
%A Tu, Kewei
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F yang-tu-2022-bottom
%X Constituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans. In this work, we cast nested NER to constituency parsing and propose a novel pointing mechanism for bottom-up parsing to tackle both tasks. The key idea is based on the observation that if we traverse a constituency tree in post-order, i.e., visiting a parent after its children, then two consecutively visited spans would share a boundary. Our model tracks the shared boundaries and predicts the next boundary at each step by leveraging a pointer network. As a result, it needs only linear steps to parse and thus is efficient. It also maintains a parsing configuration for structural consistency, i.e., always outputting valid trees. Experimentally, our model achieves the state-of-the-art performance on PTB among all BERT-based models (96.01 F1 score) and competitive performance on CTB7 in constituency parsing; and it also achieves strong performance on three benchmark datasets of nested NER: ACE2004, ACE2005, and GENIA. Our code will be available at https://github.com/xxxxx.
%R 10.18653/v1/2022.acl-long.171
%U https://aclanthology.org/2022.acl-long.171
%U https://doi.org/10.18653/v1/2022.acl-long.171
%P 2403-2416

Markdown (Informal)

[Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://aclanthology.org/2022.acl-long.171) (Yang & Tu, ACL 2022)

• Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks (Yang & Tu, ACL 2022)

ACL

• Songlin Yang and Kewei Tu. 2022. Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2403–2416, Dublin, Ireland. Association for Computational Linguistics.