iMETRE: Incorporating Markers of Entity Types for Relation Extraction

• URL: http://arxiv.org/abs/2307.00132v1
• Date: Fri, 30 Jun 2023 20:54:41 GMT
• Title: iMETRE: Incorporating Markers of Entity Types for Relation Extraction
• Authors: N Harsha Vardhan, Manav Chaudhary
• Abstract summary: Sentence-level relation extraction aims to identify the relationship between 2 entities given a contextual sentence. In this paper, we approach the task of relationship extraction in the financial dataset REFinD.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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