Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks

Songlin Yang, Kewei Tu


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.
Anthology ID:
2022.acl-long.171
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2403–2416
Language:
URL:
https://aclanthology.org/2022.acl-long.171
DOI:
10.18653/v1/2022.acl-long.171
Bibkey:
Cite (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.
Cite (Informal):
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks (Yang & Tu, ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.171.pdf
Code
 sustcsonglin/pointer-net-for-nested
Data
GENIAPenn Treebank