Domain-Transferable Method for Named Entity Recognition Task
This work addresses the problem of data scarcity for training NER models in specialized domains, which is a common bottleneck for researchers and practitioners.
This paper proposes a method to train domain-specific Named Entity Recognition (NER) models without requiring human-labelled data for the target domain. It leverages existing supervision and neural models learning from each other to achieve this.
Named Entity Recognition (NER) is a fundamental task in the fields of natural language processing and information extraction. NER has been widely used as a standalone tool or an essential component in a variety of applications such as question answering, dialogue assistants and knowledge graphs development. However, training reliable NER models requires a large amount of labelled data which is expensive to obtain, particularly in specialized domains. This paper describes a method to learn a domain-specific NER model for an arbitrary set of named entities when domain-specific supervision is not available. We assume that the supervision can be obtained with no human effort, and neural models can learn from each other. The code, data and models are publicly available.