iMETRE: Incorporating Markers of Entity Types for Relation Extraction
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 10 references that are in the map. First posted on arXiv 30 Jun 2023.
Builds on
9 of 10 references.
References · 9
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- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
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- 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
- 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 272
- 2022WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domaincited by 117
- 2021An Improved Baseline for Sentence-level Relation Extractioncited by 81
- 2023REFinD: Relation Extraction Financial Datasetcited by 17
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.