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This paper describes Team uOttawa's system for the EvaLatin 2026 shared task on Named Entity Recognition for Classical Latin, using prompt engineering of commercial LLMs (Gemini 2.5 Pro and Claude Sonnet 4-5) to achieve first place in both coarse- and fine-grained NER subtasks.
This paper proposes HomoEnsNER, a homogeneous ensemble of five GujaratiBERT models for Gujarati named entity recognition, and shows it outperforms heterogeneous alternatives that rely on architectural diversity, achieving state-of-the-art F1 on the Naamapadam test split.
DiffImaginE is a research paper proposing a diffusion-based verifier for multimodal named entity recognition, replacing deterministic imagination with conditional latent diffusion inference for more robust entity type verification.
Introduces DE-NER, a dialogue elicitation framework for zero-shot named entity recognition that uses self-play between questioner and roleplayer LLMs to clarify entity boundaries, achieving an average 3.75% F1 improvement over baselines.
LA-RL introduces a label-aware self-reflection framework for reinforcement learning in information extraction, achieving consistent improvements on named entity recognition, relation extraction, and event extraction tasks with gains of up to 20 F1 on out-of-distribution benchmarks.
This paper compares fine-tuned MahaBERT-based models with large language models (Gemini, LLaMA-3.3-70B, Gemma) for Marathi named entity recognition, finding that the specialized BERT models significantly outperform the LLMs, achieving F1-scores of 0.88–0.91 versus 0.57–0.69.
Proposes FSE, a Fast-Slow Experts span-based NER model for continual learning, achieving state-of-the-art performance on CLNER benchmarks.
This paper introduces ARI, a framework that uses retrieval-augmented large language models to restore illegible portions of historical documents, significantly improving named entity restoration by combining implicit LLM knowledge with explicitly retrieved external historical context.
This paper evaluates three NLP approaches (NER, keyword extraction, topic modelling) for automating keyword extraction in crowdsourced collections, using the Their Finest Hour Online Archive as a case study. It finds extractive models like open-weight ones are preferable for responsible deployment, while generative AI poses accountability risks.
This paper proposes LC-ICL, a novel few-shot technique that uses both correct and incorrect examples with error-cause labels to improve large language models' performance on information extraction tasks like named entity recognition and relation extraction.
This paper systematically studies how temporal metadata can be structurally embedded into named entity recognition (NER) models for historical texts. Experiments with absolute and relative temporal representations injected via early or late fusion mechanisms show that late fusion strategies yield more robust performance on French and German historical datasets.
This paper presents the results of HIPE-2026, the third edition of the HIPE evaluation series, which focuses on temporally grounded person-place relation extraction from multilingual historical documents in French, German, and English. Seventeen participating teams were evaluated on predictive accuracy, computational efficiency, and cross-domain generalization.
AAbAAC is a manually annotated corpus of 115 PubMed abstracts for autoimmunity information extraction, focusing on entities like autoimmune diseases and autoantibodies. The study demonstrates improved NER performance after fine-tuning on this corpus.
This paper investigates instruction finetuning of DeepSeek-R1-8B using LoRA and NEFTune for financial named-entity recognition, achieving a micro-F1 of 0.912 and outperforming several baseline models.
SMADE-IE is a sparse multi-agent framework for zero-shot information extraction that uses an Adaptive Mode Selector and Evidence-Driven Debate mechanism with Toulmin-style argumentation and Bayesian updates to outperform existing baselines on 9 benchmarks across NER, RE, and JERE tasks while improving token efficiency.
This paper introduces ChristBERT, a family of domain-specific RoBERTa-based language models for German clinical NLP, and evaluates three domain adaptation strategies (continued pre-training, pre-training from scratch, and vocabulary adaptation) on medical named entity recognition and text classification tasks, achieving state-of-the-art results.
This paper introduces BioConCal, a supervised scorer that uses inference-time panel and candidate features to rank biomedical entity candidates surfaced by LLM panels, significantly improving over raw agreement for curator triage.
This paper presents a specialty-specific medical language model for extracting information from clinical narratives about immune-mediated and infectious diseases, using a BiLSTM-CNN-Char architecture trained on a curated corpus of 371 case reports, achieving an F1 score of 0.89.
LELA is an LLM-based entity linking framework that combines zero-shot NER and entity disambiguation into an end-to-end Python library, validated across diverse settings.
Introduces ReDose, a dataset of 6,435 Reddit posts annotated for drug, dose, and effect entities, and benchmarks various models including BiomedBERT, Llama-3 70B, and GPT-4 for extraction.