DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition
Minghao Tang, Peng Zhang, Yongquan He, Yongxiu Xu, Chengpeng Chao, Hongbo Xu
Abstract
Cross-domain named entity recognition aims to improve performance in a target domain with shared knowledge from a well-studied source domain. The previous sequence-labeling based method focuses on promoting model parameter sharing among domains. However, such a paradigm essentially ignores the domain-specific information and suffers from entity type conflicts. To address these issues, we propose a novel machine reading comprehension based framework, named DoSEA, which can identify domain-specific semantic differences and mitigate the subtype conflicts between domains. Concretely, we introduce an entity existence discrimination task and an entity-aware training setting, to recognize inconsistent entity annotations in the source domain and bring additional reference to better share information across domains. Experiments on six datasets prove the effectiveness of our DoSEA. Our source code can be obtained from https://github.com/mhtang1995/DoSEA.- Anthology ID:
- 2022.coling-1.188
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2147–2156
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.188
- DOI:
- Cite (ACL):
- Minghao Tang, Peng Zhang, Yongquan He, Yongxiu Xu, Chengpeng Chao, and Hongbo Xu. 2022. DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2147–2156, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition (Tang et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2022.coling-1.188.pdf
- Code
- mhtang1995/dosea
- Data
- CrossNER