Abstract
Recently, cross-domain named entity recognition (cross-domain NER), which can reduce the high data annotation costs faced by fully-supervised methods, has drawn attention. Most competitive approaches mainly rely on pre-trained language models like BERT to represent words. As such, the original chaotic representations may bring challenges (e.g., entity span detection errors and entity type misclassification) for them. Motivated by this, this proposal proposes to improve cross-domain NER by refining the original representations.
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Notes
- 1.
Here entity types are pre-defined, such as location, organization, and so on.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 62076100, in part by the Fundamental Research Funds for the Central Universities, SCUT under Grant x2rjD2220050, in part by the Science and Technology Planning Project of Guangdong Province under Grant 2020B0101100002, in part by the Hong Kong Research Council under Grants PolyU 11204919 and C1031-18G, and in part by the Internal Research from the Hong Kong Polytechnic University under Grant 1.9B0V.
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Xu, J., Cai, Y. (2023). Improving Cross-Domain Named Entity Recognition from the Perspective of Representation. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13946. Springer, Cham. https://doi.org/10.1007/978-3-031-30678-5_65
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