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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

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 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. 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.

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