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FiNER-ORD: Financial Named Entity Recognition Open Research Dataset

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arxiv 2302.11157 v2 pith:TAA6E7KM submitted 2023-02-22 cs.CL cs.IR

Agam Shah , Abhinav Gullapalli , Ruchit Vithani , Michael Galarnyk , Sudheer Chava This is my paper · ORCID
classification cs.CLcs.IR
keywords datasetfiner-ordfinancialmodelsopenresearchbenchmarkdomain-specific
verification ladder T0 review T1 audit T2 compute T3 formal
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Over the last two decades, the development of the CoNLL-2003 named entity recognition (NER) dataset has helped enhance the capabilities of deep learning and natural language processing (NLP). The finance domain, characterized by its unique semantic and lexical variations for the same entities, presents specific challenges to the NER task; thus, a domain-specific customized dataset is crucial for advancing research in this field. In our work, we develop the first high-quality English Financial NER Open Research Dataset (FiNER-ORD). We benchmark multiple pre-trained language models (PLMs) and large-language models (LLMs) on FiNER-ORD. We believe our proposed FiNER-ORD dataset will open future opportunities to use FiNER-ORD as a benchmark for financial domain-specific NER and NLP tasks. Our dataset, models, and code are publicly available on GitHub and Hugging Face under CC BY-NC 4.0 license.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset

    cs.CL 2026-03 conditional novelty 6.0 of 10

    CrimeNER-db is a new, publicly released 1,568-document manually annotated corpus for crime-domain NER with a coarse/fine label hierarchy and zero-/few-shot benchmark results.

  2. SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation

    cs.CL 2024-12 reject novelty 5.0 of 10

    Small fine-tuned models on SusGen-30K are reported to nearly match GPT-4 on financial and ESG tasks, with a new TCFD-Bench benchmark, though the comparison is biased.

  3. Financial Named Entity Recognition: How Far Can LLM Go?

    cs.CL 2025-01 conditional novelty 4.0 of 10

    Generic LLMs score lower than fine-tuned BERT and RoBERTa on financial NER, but few-shot prompting narrows the gap and reveals five recurring error types.

  4. Open FinLLM Leaderboard: Towards Financial AI Readiness

    cs.CE 2025-01 conditional novelty 3.0 of 10

    The paper presents an open, continuously updated FinLLM leaderboard that aggregates existing financial benchmarks and demos for comparing models.

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