用NLP自动分类用户报修文本，识别故障电脑部件。
Classification of User Reports for Detection of Faulty Computer Components using NLP Models: A Case Study
- 构建341条用户报修文本数据集，用NLP模型分类故障部件。
- 在自建数据集上达到79%分类准确率。
- 适合关注智能客服、故障诊断的工程师和产品团队。
计算机制造商通常提供用户报告故障的平台，但这些平台对文本报告的利用能力有限，阻碍用户以自然语言描述问题。本文提出一种基于自然语言处理（NLP）的新方法，用于分类用户报告，以检测故障的计算机组件，如CPU、内存、主板、显卡等。研究构建了一个包含341条用户报告的数据集，涵盖多种来源。通过大量实验评估，该方法在自建数据集上取得了79%的分类准确率，证明了NLP在自动化故障定位中的有效性。
原文摘要 · Abstract (English)
Computer manufacturers typically offer platforms for users to report faults. However, there remains a significant gap in these platforms' ability to effectively utilize textual reports, which impedes users from describing their issues in their own words. In this context, Natural Language Processing (NLP) offers a promising solution, by enabling the analysis of user-generated text. This paper presents an innovative approach that employs NLP models to classify user reports for detecting faulty computer components, such as CPU, memory, motherboard, video card, and more. In this work, we build a dataset of 341 user reports obtained from many sources. Additionally, through extensive experimental evaluation, our approach achieved an accuracy of 79% with our dataset.
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