FiNER-ORD: Financial Named Entity Recognition Open Research Dataset
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909307658829824 |
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| author | Shah, Agam Gullapalli, Abhinav Vithani, Ruchit Galarnyk, Michael Chava, Sudheer |
| author_facet | Shah, Agam Gullapalli, Abhinav Vithani, Ruchit Galarnyk, Michael Chava, Sudheer |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_11157 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FiNER-ORD: Financial Named Entity Recognition Open Research Dataset Shah, Agam Gullapalli, Abhinav Vithani, Ruchit Galarnyk, Michael Chava, Sudheer Computation and Language Information Retrieval 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. |
| title | FiNER-ORD: Financial Named Entity Recognition Open Research Dataset |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2302.11157 |