Improving Named Entity Recognition via Bridge-based Domain Adaptation (2023)

Abstract: Recent studies have shown remarkable success in cross-domain named entity recognition (cross-domain NER). Despite the promising results, existing methods mainly utilize pre-training language models like BERT to represent words. As such, the original chaotic representations may challenge them to distinguish entity types of entities, leading to entity type misclassification. To this end, we attempt to utilize contrastive learning to refine the original representations and propose a model-agnostic framework named MoCL for cross-domain NER. Additionally, we respectively combine MoCL with two distinctive cross-domain NER methods and two pre-training language models to explore its generalization ability. Empirical results on seven domains show the effectiveness and good generalization ability of MoCL.

## introduction given a sentence, named entity recognition (ner) aims to extract entities and classify them into predefined entity types @xcite . as shown in according to the tagging scheme, previously supervised cross-domain ner approaches can be † corresponding author. ‡ the results are predicted by a state-of-the-art model @xcite . ## model this paper proposes a contrastive learning-based framework mocl for cross-domain ner, which facilitates the ability to discriminate entities with different entity types. mocl mainly consists of two modules: the base cross-ner model and the contrastive learning framework. we first introduce the base cross-domain ner model (section 2.1) and then describe the contrastive learning framework (section 2.2). finally, we present the training procedure (section 2.3). the whole architecture of mocl is shown in figure 3 . ## related work due to the capability of extracting useful information and benefiting many nlp applications (e.g., information retrieval @xcite and question answering @xcite ), ner appeals to many researchers @xcite @xcite @xcite @xcite @xcite @xcite . recently, to reduce the huge cost of annotating data, researchers start to explore crossdomain ner methods. according to whether the labeled data of the target domain are used or not, these methods can be classified into unsupervised @xcite @xcite @xcite @xcite and supervised @xcite @xcite . this paper focuses on the latter and according to the tagging scheme, supervised cross-domain ner methods can be classified into compositional labeling-based @xcite and modular learning-based @xcite . compared with previous studies, we attempt to improve both kinds of methods from the perspective of representation. in particular, a model-agnostic framework mocl is introduced to refine the original chaotic representations by contrastive learning, motivated by its success in computer vision @xcite @xcite @xcite @xcite . ## conclusion this paper explores utilizing contrastive learning to gain discriminative entity representations in the field of cross-domain named entity recognition. to guide contrastive learning at the entity level, we explored two bridges to capture different relations of entities at different granularities. additionally, our framework is model-agnostic, so we respectively integrate it into two existing cross-ner baselines and two different pre-training language models to evaluate its generalization ability. the experimental results show that mocl could help models learn discriminative representations and it has good generalization ability. in terms of the limitation, currently, we mainly evaluate mocl under the single-source cross-domain setting. we plan to further extend it to multi-source cross-domain settings. moreover, the interaction between named entity recognition and relation extraction can be considered to improve performance in the future.