Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction

• URL: http://arxiv.org/abs/2308.04534v1
• Date: Tue, 8 Aug 2023 18:56:52 GMT
• Title: Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction
• Authors: Stefan Pasch, Dimitrios Petridis
• Abstract summary: 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. We fine-tuned the transformer-based language model roberta-large for text classification by utilizing a labeled training set to predict the entity relations. As a result of our methodology, we achieved the 1st place ranking on the competition's public leaderboard.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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