An Empirical Cross Domain-Specific Entity Recognition with Domain Vector
Wei Chen, Songqiao Han, Hailiang Huang · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Recognizing terminology entities across domains from professional texts is an important but challenging task in NLP. Most existing methods focus on recognizing generic entities, but few methods are to recognize the domain-specific entities across domains due to the very large discrepancy of entity representations between the source and target domains. To address this issue, we introduce domain vectors and context vectors to represent domain-specific semantics of entities and domain-irrelevant semantics of the context words, respectively. Based on the two types of vectors, we present a simple yet effective novel cross-domain named entity recognition approach, which aligns entity distributions between domains and separates entity distributions from context distributions for easily identifying entities. Experimental results demonstrate that the proposed approach can obtain significant improvement compared to existing cross-domain NER methods.