Improving LLM-based opinion expression identification with dependency syntax

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qiujing Xu , Peiming Guo , Fei Li , Meishan Zhang , Donghong Ji
{"title":"Improving LLM-based opinion expression identification with dependency syntax","authors":"Qiujing Xu ,&nbsp;Peiming Guo ,&nbsp;Fei Li ,&nbsp;Meishan Zhang ,&nbsp;Donghong Ji","doi":"10.1016/j.patrec.2025.07.012","DOIUrl":null,"url":null,"abstract":"<div><div>Opinion expression identification (OEI), a crucial task in fine-grained opinion mining, has received long-term attention for several decades. Recently, large language models (LLMs) have demonstrated substantial potential on the task. However, structural-aware syntax features, which have proven highly effective for encoder-based OEI models, remain challenging to be explored under the LLM paradigm. In this work, we introduce a novel approach that successfully enhances LLM-based OEI with the aid of dependency syntax. We start with a well-formed prompt learning framework for OEI, and then enrich the prompting text with syntax information from an off-the-shelf dependency parser. To mitigate the negative impact of irrelevant dependency structures, we employ a BERT-based CRF model as a retriever to select only salient dependencies. Experiments on three benchmark datasets covering English, Chinese and Portuguese indicate that our method is highly effective, resulting in significant improvements on all datasets. We also provide detailed analysis to understand our method in-depth.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"197 ","pages":"Pages 81-87"},"PeriodicalIF":3.3000,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167865525002648","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Opinion expression identification (OEI), a crucial task in fine-grained opinion mining, has received long-term attention for several decades. Recently, large language models (LLMs) have demonstrated substantial potential on the task. However, structural-aware syntax features, which have proven highly effective for encoder-based OEI models, remain challenging to be explored under the LLM paradigm. In this work, we introduce a novel approach that successfully enhances LLM-based OEI with the aid of dependency syntax. We start with a well-formed prompt learning framework for OEI, and then enrich the prompting text with syntax information from an off-the-shelf dependency parser. To mitigate the negative impact of irrelevant dependency structures, we employ a BERT-based CRF model as a retriever to select only salient dependencies. Experiments on three benchmark datasets covering English, Chinese and Portuguese indicate that our method is highly effective, resulting in significant improvements on all datasets. We also provide detailed analysis to understand our method in-depth.
使用依赖语法改进基于llm的意见表达识别
意见表达识别是细粒度意见挖掘中的一项重要任务,几十年来一直受到人们的关注。最近,大型语言模型(llm)在该任务上显示出了巨大的潜力。然而,对于基于编码器的OEI模型来说,结构感知语法特性已经被证明是非常有效的,但在LLM范式下仍有待探索。在这项工作中,我们引入了一种新的方法,通过依赖语法成功地增强了基于llm的OEI。我们从一个用于OEI的格式良好的提示学习框架开始,然后用来自现成依赖项解析器的语法信息丰富提示文本。为了减轻不相关依赖结构的负面影响,我们采用基于bert的CRF模型作为检索器,只选择显著的依赖。在英语、中文和葡萄牙语三个基准数据集上的实验表明,我们的方法是非常有效的,在所有数据集上都有显著的改进。我们还提供详细的分析,以深入了解我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Pattern Recognition Letters
Pattern Recognition Letters 工程技术-计算机:人工智能
CiteScore
12.40
自引率
5.90%
发文量
287
审稿时长
9.1 months
期刊介绍: Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition. Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信