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Type-Balanced Contextual Learning for Incremental Named Entity Recognition

Paper recorded by Signals 4 on 2026-08-31 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.CL · 自然语言处理 · first seen 2026-09-01

Abstract

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 pseu

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#158 most recent of 186 cs.CL papers we have recorded · ↑ newer: Improving Information Extraction with Learned Queries · ↓ older: Language-Statistical Analysis of Neural Audio Codec Tokens Across Arch
Cite this page: Type-Balanced Contextual Learning for Incremental Named Entity Recognition: the #158 most recent of 186 cs.CL papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/type-balanced-contextual-learning-for-incremental-named-entity-recognition.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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