A Boundary Offset Prediction Network for Named Entity Recognition
Minghao Tang Yongquan He Yongxiu Xu Hongbo Xu Wenyuan Zhang Yang Lin
Named entity recognition (NER) is a fundamental task in natural language
processing that aims to identify and classify named entities in text. However,
span-based methods for NER typically assign entity types to text spans,
resulting in an imbalanced sample space and neglecting the connections between
non-entity and entity spans. To address these issues, we propose a novel
approach for NER, named the Boundary Offset Prediction Network (BOPN), which
predicts the boundary offsets between candidate spans and their nearest entity
spans. By leveraging the guiding semantics of boundary offsets, BOPN
establishes connections between non-entity and entity spans, enabling
non-entity spans to function as additional positive samples for entity
detection. Furthermore, our method integrates entity type and span
representations to generate type-aware boundary offsets instead of using entity
types as detection targets. We conduct experiments on eight widely-used NER
datasets, and the results demonstrate that our proposed BOPN outperforms
previous state-of-the-art methods.