Cross-domain NER under a Divide-and-Transfer Paradigm

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xinghua Zhang, Bowen Yu, Xin Cong, Taoyu Su, Quangang Li, Tingwen Liu, Hongbo Xu
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引用次数: 0

Abstract

Cross-domain Named Entity Recognition (NER) transfers knowledge learned from a rich-resource source domain to improve the learning in a low-resource target domain. Most existing works are designed based on the sequence labeling framework, defining entity detection and type prediction as a monolithic process. However, they typically ignore the discrepant transferability of these two sub-tasks: the former locating spans corresponding to entities is largely domain-robust, while the latter owns distinct entity types across domains. Combining them into an entangled learning problem may contribute to the complexity of domain transfer. In this work, we propose the novel divide-and-transfer paradigm in which different sub-tasks are learned using separate functional modules for respective cross-domain transfer. To demonstrate the effectiveness of divide-and-transfer, we concretely implement two NER frameworks by applying this paradigm with different cross-domain transfer strategies. Experimental results on 10 different domain pairs show the notable superiority of our proposed frameworks. Experimental analyses indicate that significant advantages of the divide-and-transfer paradigm over prior monolithic ones originate from its better performance on low-resource data and a much greater transferability. It gives us a new insight into cross-domain NER. Our code is available at our github.

分而治之范式下的跨域 NER
跨领域命名实体识别(NER)将从资源丰富的源领域学习到的知识转移到资源匮乏的目标领域,从而提高学习效率。现有的大多数工作都是基于序列标注框架设计的,将实体检测和类型预测定义为一个整体过程。然而,它们通常忽略了这两个子任务的不同可转移性:前者定位与实体相对应的跨度在很大程度上是不受领域限制的,而后者则拥有跨领域的不同实体类型。将它们结合成一个纠缠不清的学习问题可能会增加领域转移的复杂性。在这项工作中,我们提出了新颖的 "分而治之 "范式,即使用不同的功能模块学习不同的子任务,以实现各自的跨领域转移。为了证明 "分割-转移 "的有效性,我们采用不同的跨域转移策略,具体实施了两个 NER 框架。在 10 个不同域对上的实验结果表明,我们提出的框架具有显著的优越性。实验分析表明,与之前的单一范式相比,"分割-转移 "范式的显著优势在于其在低资源数据上的更好性能和更高的可转移性。它让我们对跨域 NER 有了新的认识。我们的代码可在 github 上获取。
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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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