Cone：无监督对比观点抽取
Cone: Unsupervised Contrastive Opinion Extraction
AI总结：
提出无监督对比观点抽取模型Cone，结合对比学习与迭代聚类细化，无需标签即可抽取对比观点并量化方面流行度，在酒店和疫苗推特数据集上优于基线。
摘要：
对比观点抽取旨在针对共同方面或主题，提取以正面和负面观点组织的结构化摘要或关键点。近期无监督关键点抽取工作主要基于句子聚类或基于文本中表达观点流行度的观点摘要。然而，这些方法往往产生包含不连贯句子、冲突观点和冗余方面的方面聚类。为解决这些问题，我们提出了一种新颖的无监督对比观点抽取模型，名为Cone，该模型通过结合对比学习与迭代方面/观点聚类细化，基于伪方面和伪观点标签学习解缠的潜在方面与观点表示。除了能够抽取对比观点外，该模型还能量化方面及其相关观点分布的相对流行度。该模型已在酒店评论数据集和关于新冠疫苗的Twitter数据集上进行了评估。结果表明，尽管不使用标签监督或方面标注的种子词，Cone在对比观点抽取上优于多个竞争基线。Cone的结果可用于在线提供更好的产品和服务推荐。
英文摘要：
Contrastive opinion extraction aims to extract a structured summary or key points organised as positive and negative viewpoints towards a common aspect or topic. Most recent works for unsupervised key point extraction is largely built on sentence clustering or opinion summarisation based on the popularity of opinions expressed in text. However, these methods tend to generate aspect clusters with incoherent sentences, conflicting viewpoints, redundant aspects. To address these problems, we propose a novel unsupervised Contrastive OpinioN Extraction model, called Cone, which learns disentangled latent aspect and sentiment representations based on pseudo aspect and sentiment labels by combining contrastive learning with iterative aspect/sentiment clustering refinement. Apart from being able to extract contrastive opinions, it is also able to quantify the relative popularity of aspects and their associated sentiment distributions. The model has been evaluated on both a hotel review dataset and a Twitter dataset about COVID vaccines. The results show that despite using no label supervision or aspect-denoted seed words, Cone outperforms a number of competitive baselines on contrastive opinion extraction. The results of Cone can be used to offer a better recommendation of products and services online.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
