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

Stefan Pasch, Dimitrios Petridis
8/8/2023
cs.CL

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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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Cite this paper

@article{pasch2023ahead,
  title  = {Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction},
  author = {Stefan Pasch and Dimitrios Petridis},
  year   = {2023},
  eprint = {2308.04534},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.04534v1}
}

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