iMETRE: Incorporating Markers of Entity Types for Relation Extraction
This work addresses relation extraction for financial data, but it appears incremental as it builds on existing marker-based methods.
The paper tackled sentence-level relation extraction on the financial dataset REFinD by incorporating typed entity markers and fine-tuning models, achieving an F1 score of 69.65% on the validation set.
Sentence-level relation extraction (RE) aims to identify the relationship between 2 entities given a contextual sentence. While there have been many attempts to solve this problem, the current solutions have a lot of room to improve. In this paper, we approach the task of relationship extraction in the financial dataset REFinD. Our approach incorporates typed entity markers representations and various models finetuned on the dataset, which has allowed us to achieve an F1 score of 69.65% on the validation set. Through this paper, we discuss various approaches and possible limitations.