利用仅编码器大型语言模型进行移动应用评论特征提取
Leveraging Encoder-only Large Language Models for Mobile App Review Feature Extraction
AI总结：
本研究将移动应用评论特征提取重新定义为有监督词元分类任务，利用仅编码器大型语言模型，通过扩展预训练和实例选择优化微调，有效提升了特征提取的精确率、召回率和性能效率。
摘要：
由于用户生成文档存在质量低、主观偏见和噪声内容等问题，移动应用评论分析面临独特挑战。从这些评论中提取特征对于特征优先级排序和情感分析等任务至关重要，但这仍是一项艰巨的任务。同时，基于Transformer架构的仅编码器模型在多个软件工程流程的分类和信息提取任务中展现出良好效果。本研究探讨了仅编码器大型语言模型能够增强移动应用评论特征提取的假设。通过利用工业环境中的众包标注，我们将特征提取重新定义为有监督的词元分类任务。我们的方法包括利用大规模用户评论语料库扩展这些模型的预训练以提升上下文理解，并采用实例选择技术来优化模型微调。实证评估表明，该方法提高了提取特征的精确率和召回率，并提升了性能效率。主要贡献包括一种新颖的特征提取方法、带标注的数据集、扩展的预训练模型，以及用于高性价比微调的实例选择机制。本研究为将大型语言模型应用于移动应用评论的自然语言处理任务提供了实用方法和实证证据，提升了特征提取的性能。
英文摘要：
Mobile app review analysis presents unique challenges due to the low quality, subjective bias, and noisy content of user-generated documents. Extracting features from these reviews is essential for tasks such as feature prioritization and sentiment analysis, but it remains a challenging task. Meanwhile, encoder-only models based on the Transformer architecture have shown promising results for classification and information extraction tasks for multiple software engineering processes. This study explores the hypothesis that encoder-only large language models can enhance feature extraction from mobile app reviews. By leveraging crowdsourced annotations from an industrial context, we redefine feature extraction as a supervised token classification task. Our approach includes extending the pre-training of these models with a large corpus of user reviews to improve contextual understanding and employing instance selection techniques to optimize model fine-tuning. Empirical evaluations demonstrate that this method improves the precision and recall of extracted features and enhances performance efficiency. Key contributions include a novel approach to feature extraction, annotated datasets, extended pre-trained models, and an instance selection mechanism for cost-effective fine-tuning. This research provides practical methods and empirical evidence in applying large language models to natural language processing tasks within mobile app reviews, offering improved performance in feature extraction.
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。
