ESNERA: Empirical and semantic named entity alignment for named entity dataset merging
This work addresses the bottleneck of expensive dataset creation for NER by providing an efficient, interpretable, and scalable solution for integrating multi-source corpora, though it is incremental in nature.
The paper tackled the problem of merging named entity recognition datasets by proposing an automatic label alignment method based on empirical and semantic similarities, which enabled effective dataset integration and enhanced NER performance in a low-resource financial domain.
Named Entity Recognition (NER) is a fundamental task in natural language processing. It remains a research hotspot due to its wide applicability across domains. Although recent advances in deep learning have significantly improved NER performance, they rely heavily on large, high-quality annotated datasets. However, building these datasets is expensive and time-consuming, posing a major bottleneck for further research. Current dataset merging approaches mainly focus on strategies like manual label mapping or constructing label graphs, which lack interpretability and scalability. To address this, we propose an automatic label alignment method based on label similarity. The method combines empirical and semantic similarities, using a greedy pairwise merging strategy to unify label spaces across different datasets. Experiments are conducted in two stages: first, merging three existing NER datasets into a unified corpus with minimal impact on NER performance; second, integrating this corpus with a small-scale, self-built dataset in the financial domain. The results show that our method enables effective dataset merging and enhances NER performance in the low-resource financial domain. This study presents an efficient, interpretable, and scalable solution for integrating multi-source NER corpora.