论文标题
用于检测缺陷报告和改进请求的模式学习
Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews
论文作者
论文摘要
在线评论是了解客户的重要反馈来源。在这项研究中，我们遵循针对这种缺乏可行见解的新方法，通过将评论分类为缺陷报告和改进请求。与基于专家规则的传统分类方法不同，我们通过采用能够通过遗传编程学习词典语义模式的监督系统来减少体力劳动。此外，我们尝试使用远程监督的SVM，该SVM利用图案产生的嘈杂标签。使用应用程序评论的真实数据集，我们表明自动学习的模式优于手动创建的模式，即生成。同样，远程监督的SVM模型也不落后于基于模式的解决方案，显示出注释数据的量受到限制时该方法的有用性。
Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target this absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones, to be generated. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.
