In the context of the ACM KDF-SIGIR 2023 competition, we undertook an entity relation task on a dataset of financial entity relations called REFind. Our top-performing solution involved a multi-step approach. Initially, we inserted the provided entities at their corresponding locations within the text. Subsequently, we fine-tuned the transformer-based language model roberta-large for text classification by utilizing a labeled training set to predict the entity relations. Lastly, we implemented a post-processing phase to identify and handle improbable predictions generated by the model. As a result of our methodology, we achieved the 1st place ranking on the competition's public leaderboard.
@article{arxiv.2308.04534,
title = {Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction},
author = {Stefan Pasch and Dimitrios Petridis},
journal= {arXiv preprint arXiv:2308.04534},
year = {2023}
}
Comments
Stefan Pasch, Dimitrios Petridis 2023. Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction. ACM SIGIR: The 4th Workshop on Knowledge Discovery from Unstructured Data in Financial Services (SIGIR-KDF '23)