Dual Contrastive Learning for Cross-Domain Named Entity Recognition
Article No.: 163, Pages 1 - 33
Abstract
Benefiting many information retrieval applications, named entity recognition (NER) has shown impressive progress. Recently, there has been a growing trend to decompose complex NER tasks into two subtasks (e.g., entity span detection (ESD) and entity type classification (ETC), to achieve better performance. Despite the remarkable success, from the perspective of representation, existing methods do not explicitly distinguish non-entities and entities, which may lead to ESD errors. Meanwhile, they do not explicitly distinguish entities with different entity types, which may lead to entity type misclassification. As such, the limited representation abilities may challenge some competitive NER methods, leading to unsatisfactory performance, especially in the low-resource setting (e.g., cross-domain NER). In light of these challenges, we propose to utilize contrastive learning to refine the original chaotic representations and learn the generalized representations for cross-domain NER. In particular, this article proposes a dual contrastive learning model (Dual-CL), which respectively utilizes a token-level contrastive learning module and a sentence-level contrastive learning module to enhance ESD, ETC for cross-domain NER. Empirical results on 10 domain pairs under two different settings show that Dual-CL achieves better performances than compared baselines in terms of several standard metrics. Moreover, we conduct detailed analyses to are presented to better understand each component’s effectiveness.
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Index Terms
- Dual Contrastive Learning for Cross-Domain Named Entity Recognition
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Publication History
Published: 18 October 2024
Online AM: 20 July 2024
Accepted: 16 June 2024
Revised: 09 February 2024
Received: 16 July 2023
Published in TOIS Volume 42, Issue 6
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- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities, South China University of Technology
- Science and Technology Planning Project of Guangdong Province
- Guangdong Provincial Fund for Basic and Applied Basic Research—Regional Joint Fund Project (Key Project)
- Guangdong Provincial Natural Science Foundation for Outstanding Youth Team Project
- Chinese Association for Artificial Intelligence (CAAI)-Huawei MindSpore Open Fund, and the China Computer Federation (CCF)-Zhipu AI Large Model Fund. This research is also supported by NExT Research Center
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