社区的力量：一个基于网络社区发现的自动标注文本分类模型
The Power of Communities: A Text Classification Model with Automated Labeling Process Using Network Community Detection
摘要：
文本分类是机器学习和人工智能研究中最关键的领域之一。它已经被积极地应用于许多商业应用中，例如会话智能系统、新闻文章分类、情感分析、情感检测系统以及我们日常生活中的许多其他推荐系统。有监督文本分类模型的一个问题是，模型的性能在很大程度上取决于通常由人类完成的数据标记的质量。在这项研究中，我们提出了一个新的网络社区发现为基础的方法来自动标记和分类文本数据到多类值空间。具体来说，我们建立网络的句子作为网络节点和成对的余弦相似度之间的词频反转的文档频率（TFIDF）的向量表示的句子作为网络链接的权重。我们使用Louvain方法来检测句子网络中的社区。我们在人类标记数据和网络社区发现标记数据上对支持向量机和随机森林模型进行了训练和测试。实验结果表明，基于网络社区发现标记数据的模型比基于人工标记数据的模型的分类准确率高2.68-3.75%。我们的方法可能有助于更准确的会话智能和其他文本分类系统的发展。
英文摘要：
Text classification is one of the most critical areas in machine learning and artificial intelligence research. It has been actively adopted in many business applications such as conversational intelligence systems, news articles categorizations, sentiment analysis, emotion detection systems, and many other recommendation systems in our daily life. One of the problems in supervised text classification models is that the models' performance depends heavily on the quality of data labeling that is typically done by humans. In this study, we propose a new network community detection-based approach to automatically label and classify text data into multiclass value spaces. Specifically, we build networks with sentences as the network nodes and pairwise cosine similarities between the Term Frequency-Inversed Document Frequency (TFIDF) vector representations of the sentences as the network link weights. We use the Louvain method to detect the communities in the sentence networks. We train and test the Support Vector Machine and the Random Forest models on both the human-labeled data and network community detection labeled data. Results showed that models with the data labeled by the network community detection outperformed the models with the human-labeled data by 2.68-3.75% of classification accuracy. Our method may help developments of more accurate conversational intelligence and other text classification systems.
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。
