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kevinyu66 at SemEval-2026 Task 3: A Retrieval-Augmented LLM System for Aspect–Opinion Triplet Extraction

Year: 2026

Type: conference-paper

Abstract: This paper describes our system used in the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis.To address the inherent subjectivity and nuanced emotional expressions in this task, we propose a Retrieval-Augmented Generation (RAG) framework based on Large Language Models (LLMs) for sentiment triplet extraction.Our approach leverages a dynamic retrieval mechanism to identify semantically similar training examples, which are then integrated into the prompts as in-context demonstrations.This strategy effectively guides the model's inference process by providing relevant linguistic patterns and emotional contexts.Our implementation is available at https://github. com/Kevinyu66/dimaste.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.16

Authors: Kuanlin Yu, Wen-Ni Liu

Institutions: National Cheng Kung University, National Yang Ming Chiao Tung University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7166812225

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