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        <title>Cross-domain Named Entity Recognition via Graph Matching</title>
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        <namePart type="given">Junhao</namePart>
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    <abstract>Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource general domain and then adapt the model to specific domains. Due to the mismatch problem between entity types across domains, the wide knowledge in the general domain can not effectively transfer to the target domain NER model. To this end, we model the label relationship as a probability distribution and construct label graphs in both source and target label spaces. To enhance the contextual representation with label structures, we fuse the label graph into the word embedding output by BERT. By representing label relationships as graphs, we formulate cross-domain NER as a graph matching problem. Furthermore, the proposed method has good applicability with pre-training methods and is potentially capable of other cross-domain prediction tasks. Empirical results on four datasets show that our method outperforms a series of transfer learning, multi-task learning, and few-shot learning methods.</abstract>
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%0 Conference Proceedings
%T Cross-domain Named Entity Recognition via Graph Matching
%A Zheng, Junhao
%A Chen, Haibin
%A Ma, Qianli
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zheng-etal-2022-cross
%X Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource general domain and then adapt the model to specific domains. Due to the mismatch problem between entity types across domains, the wide knowledge in the general domain can not effectively transfer to the target domain NER model. To this end, we model the label relationship as a probability distribution and construct label graphs in both source and target label spaces. To enhance the contextual representation with label structures, we fuse the label graph into the word embedding output by BERT. By representing label relationships as graphs, we formulate cross-domain NER as a graph matching problem. Furthermore, the proposed method has good applicability with pre-training methods and is potentially capable of other cross-domain prediction tasks. Empirical results on four datasets show that our method outperforms a series of transfer learning, multi-task learning, and few-shot learning methods.
%R 10.18653/v1/2022.findings-acl.210
%U https://aclanthology.org/2022.findings-acl.210
%U https://doi.org/10.18653/v1/2022.findings-acl.210
%P 2670-2680

Markdown (Informal)

[Cross-domain Named Entity Recognition via Graph Matching](https://aclanthology.org/2022.findings-acl.210) (Zheng et al., Findings 2022)

• Cross-domain Named Entity Recognition via Graph Matching (Zheng et al., Findings 2022)

ACL

• Junhao Zheng, Haibin Chen, and Qianli Ma. 2022. Cross-domain Named Entity Recognition via Graph Matching. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2670–2680, Dublin, Ireland. Association for Computational Linguistics.