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This paper presents a method for comparing concordances of local grammars to optimize Named Entity Recognition for person names in Portuguese, achieving improved F-measure scores on the HAREM dataset.
Released en_legal_ner_ind_trf v0.1, an InLegalBERT model fine-tuned on 33,000 Indian Supreme Court judgments, achieving a 97.76% F1 score on case citations and significantly outperforming previous baselines.
ArXiv preprint identifies low information density as the root cause of NER performance collapse on noisy user-generated content and introduces the Window-Aware Optimization Module (WOM) that boosts F1 by up to 4.5% on WNUT2017.
DiZiNER is a framework that uses disagreement between multiple LLMs to refine task instructions for zero-shot named entity recognition, achieving state-of-the-art results on 14 out of 18 benchmarks and significantly reducing the performance gap between zero-shot and supervised systems.
Authors release Universal NER v2, a named-entity recognition paper presented at LREC 2026 that deliberately eschews modern scaling and tool-use trends.