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iMETRE: Incorporating Markers of Entity Types for Relation Extraction

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arxiv 2307.00132 v1 pith:FJ6U2V5X submitted 2023-06-30 cs.CL

N Harsha Vardhan , Manav Chaudhary This is my paper · ORCID
classification cs.CL
keywords extractionapproachdatasetentitymarkersrelationrelationshipvarious
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A pluggable auxiliary sentiment and dependency-path supervision module improves F1 for most tested relation extraction models on REFinD and TACRED.

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