用多视角提示学习提升产品评论中的对比观点抽取精度
Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning
- 采用多视角提示引导生成模型进行对比观点抽取
- 在英文数据集上比基线模型高1.41% F1分数
- 适合需要精准分析消费者偏好的电商研究者
对比评论对理解消费者偏好和影响购买决策至关重要。对比五元组抽取（COQE）旨在识别文本中的五个关键成分：目标实体、对比实体、对比方面、对这些方面的观点及情感极性。由于语言细微差别和传统方法的序列任务错误，从产品评论中精确提取对比信息极具挑战。为此，我们提出MTP-COQE，一种面向COQE的端到端模型。该模型利用多视角提示学习，有效引导生成模型完成对比观点挖掘任务。在Camera-COQE（英文）和VCOM（越南语）数据集上的评估表明，MTP-COQE能有效实现自动化COQE，在英文数据集上相比先前基线模型取得1.41%更高的F1分数。此外，我们设计了策略限制生成模型的创造性，确保输出符合预期；还通过数据增强缓解数据不平衡问题，防止模型偏向多数样本。
原文摘要 · Abstract (English)
Comparative reviews are pivotal in understanding consumer preferences and influencing purchasing decisions. Comparative Quintuple Extraction (COQE) aims to identify five key components in text: the target entity, compared entities, compared aspects, opinions on these aspects, and polarity. Extracting precise comparative information from product reviews is challenging due to nuanced language and sequential task errors in traditional methods. To mitigate these problems, we propose MTP-COQE, an end-to-end model designed for COQE. Leveraging multi-perspective prompt-based learning, MTP-COQE effectively guides the generative model in comparative opinion mining tasks. Evaluation on the Camera-COQE (English) and VCOM (Vietnamese) datasets demonstrates MTP-COQE's efficacy in automating COQE, achieving superior performance with a 1.41% higher F1 score than the previous baseline models on the English dataset. Additionally, we designed a strategy to limit the generative model's creativity to ensure the output meets expectations. We also performed data augmentation to address data imbalance and to prevent the model from becoming biased towards dominant samples.
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