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FiNER-ORD: Financial Named Entity Recognition Open Research Dataset

2023-02-22 · Agam Shah, Abhinav Gullapalli, Ruchit Vithani, Michael Galarnyk, Sudheer Chava

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.

📄 PDF Abstract BibTeX arXiv:2302.11157

Code (3)

gtfintechlab/finer 공식 구현
gtfintechlab/finer-ord 공식 구현 pytorch
Alex-Lyu0419/Financial-Named-Entity-Recognition-How-Far-Can-LLM-Go

Tasks

named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER

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