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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Duzhen Zhang →
“Type-Balanced Contextual Learning for Incremental Named Entity Recognition”
- LinkedLinked via arxiv author · 85%Yahan Yu →
“Type-Balanced Contextual Learning for Incremental Named Entity Recognition”
- LinkedLinked via arxiv author · 85%Xiuyi Chen →
“Type-Balanced Contextual Learning for Incremental Named Entity Recognition”
- LinkedLinked via arxiv author · 85%Chenxing Li →
“Type-Balanced Contextual Learning for Incremental Named Entity Recognition”
- LinkedLinked via arxiv author · 85%Dongdong Yu →
“Type-Balanced Contextual Learning for Incremental Named Entity Recognition”
- FuzzySimilar title/name (fuzzy) · 84%amitness/learning →
“Fuzzy title match (0.92): “Type-Balanced Contextual Learning for Incremental Named Enti” ≈ “amitness/learning””