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Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech

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arxiv 2405.06665 v1 pith:BLDE6FLS submitted 2024-05-02 cs.CL cs.IRcs.LG

Menglin Li , Kwan Hui Lim This is my paper · ORCID
classification cs.CLcs.IRcs.LG
keywords financialmodelsrelationdatasetentitiesextractionfinrelanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A pluggable auxiliary sentiment and dependency-path supervision module improves F1 for most tested relation extraction models on REFinD and TACRED.

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