SOURCE-LINKED INTELLIGENCE
Type-Balanced Contextual Learning for Incremental Named Entity Recognition
Incremental Named Entity Recognition (INER) stands as a pivotal task in information extraction, emphasizing the successive identification of new entity types within unstructured text. Faced with the continuous influx of entity types, INER grapples with two significant challenges: the widespread issue of catastrophic forgetting and the unique shift issue of the non-entity type semantics. While pseudo-labeling-based INER methods have proven effective in addressing these challenges, a previously overlooked issue arises: the biased context problem. Our analysis shows that, in new sentences, the co
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T16:15:41.000Z
First collected: 2026-09-21T06:41:57.136Z. This is not the publication date.