Local Metric NER: A new paradigm for named entity recognition from a multi-label perspective

As the field of Nested Named Entity Recognition (NNER) advances, it is marked by a growing complexity due to the increasing number of multi-label entity instances. How to more effectively identify multi-label entities and explore the correlation between labels is the focus of our work. Unlike previous models that are modeled in single-label multi-classification problems, we propose a novel multi-label local metric NER model to rethink Nested Entity Recognition from a multi-label perspective. Simultaneously, to address the significant sample imbalance problem commonly encountered in multi-label scenarios, we introduce a parts-of-speech-based strategy that significantly improves the model’s performance on imbalanced datasets. Experiments on nested, multi-label, and flat datasets verify the generalization and superiority of our model, with results surpassing the existing state-of-the-art (SOTA) on several multi-label and flat benchmarks. After a series of experimental analyses, we highlight the persistent challenges in the multi-label NER. We are hopeful that the insights derived from our work will not only provide new perspectives on the nested NER landscape but also contribute to the ongoing momentum necessary for advancing research in the field of multi-label NER. • Multi-label Named Entity Recognition (NER) is overlooked and unexplored as a specialised subfield within the domain of nested NER. • We propose a Local Metric method for rethinking Named Entity Recognition from a multi-label perspective. • Our research pioneers exploring label correlations through co-occurrence probabilities in the NER domain. • Our model surpasses the existing state-of-the-art (SOTA) models on several datasets. • We summarize current challenges and characteristics of datasets in multi-label NER, with the expectation of fostering future research.