CrossNER: Evaluating Cross-Domain Named Entity Recognition

TL;DR

This paper introduces CrossNER, a comprehensive cross-domain NER dataset with domain-specific entity types, and evaluates domain-adaptive pre-training strategies to improve cross-domain NER performance, highlighting both progress and ongoing challenges.

Contribution

The paper presents a new fully-labeled cross-domain NER dataset with domain-specific entities and explores effective domain-adaptive pre-training strategies for NER.

Findings

Domain-specific entity focus improves NER adaptation.

Domain-adaptive pre-training enhances cross-domain NER performance.

Challenges remain in fully addressing cross-domain NER difficulties.

Abstract

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack domain-specialized entity types or do not focus on a certain domain, leading to a less effective cross-domain evaluation. To address these obstacles, we introduce a cross-domain NER dataset (CrossNER), a fully-labeled collection of NER data spanning over five diverse domains with specialized entity categories for different domains. Additionally, we also provide a domain-related corpus since using it to continue pre-training language models (domain-adaptive pre-training) is effective for the domain adaptation. We then conduct comprehensive experiments to explore the effectiveness of leveraging different levels of the domain corpus and pre-training strategies to do domain-adaptive pre-training for the cross-domain task. Results show that focusing on the fractional corpus containing domain-specialized entities and utilizing a more challenging pre-training strategy in domain-adaptive pre-training are beneficial for the NER domain adaptation, and our proposed method can consistently outperform existing cross-domain NER baselines. Nevertheless, experiments also illustrate the challenge of this cross-domain NER task. We hope that our dataset and baselines will catalyze research in the NER domain adaptation area. The code and data are available at https://github.com/zliucr/CrossNER.