Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech
<|reference_start|>Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech: The Financial Relation Extraction (FinRE) task involves identifying the entities and their relation, given a piece of financial statement/text. To solve this FinRE problem, we propose a simple but effective strategy that improves the performance of pre-trained language models by augmenting them with Named Entity Recognition (NER) and Part-Of-Speech (POS), as well as different approaches to combine these information. Experiments on a financial relations dataset show promising results and highlights the benefits of incorporating NER and POS in existing models. Our dataset and codes are available at https://github.com/kwanhui/FinRelExtract.<|reference_end|>
@article{li2024enhancing,
    title={Enhancing Language Models for Financial Relation Extraction with Named
  Entities and Part-of-Speech},
    author={Menglin Li and Kwan Hui Lim},
    journal={arXiv preprint arXiv:2405.06665},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06665},
    primaryClass={cs.CL cs.IR cs.LG}
}