对比机器学习与神经符号模型在博客性别分类中的表现
Blog Data Showdown: Machine Learning vs Neuro-Symbolic Models for Gender Classification
- 采用多种文本表示与特征提取方法，比较传统算法与神经符号模型
- 神经符号模型在小数据下表现接近深度多层感知机
- 适合对可解释性要求高的分类任务研究者参考
文本分类问题，如从博客中进行性别分类，已是一个成熟的研究领域，广泛应用于市场分析、客户推荐和推荐系统。本研究对比了支持向量机（SVM）、朴素贝叶斯（NB）、逻辑回归（LR）、AdaBoost、XGBoost及其变体（SVM_R）等主流机器学习算法，以及神经符号人工智能（NeSy）。研究还考察了TF-IDF、通用句子编码器（USE）和RoBERTa等文本表示方式，以及卡方检验、互信息和主成分分析等特征提取技术。在此基础上，进一步对比了机器学习、深度学习与神经符号模型的性能。实验结果表明，在数据量有限的情况下，神经符号方法的表现可媲美强大的多层感知机（MLP）模型。未来工作将扩展知识库、嵌入类型范围及超参数配置，以深入评估神经符号方法的有效性。
原文摘要 · Abstract (English)
Text classification problems, such as gender classification from a blog, have been a well-matured research area that has been well studied using machine learning algorithms. It has several application domains in market analysis, customer recommendation, and recommendation systems. This study presents a comparative analysis of the widely used machine learning algorithms, namely Support Vector Machines (SVM), Naive Bayes (NB), Logistic Regression (LR), AdaBoost, XGBoost, and an SVM variant (SVM_R) with neuro-symbolic AI (NeSy). The paper also explores the effect of text representations such as TF-IDF, the Universal Sentence Encoder (USE), and RoBERTa. Additionally, various feature extraction techniques, including Chi-Square, Mutual Information, and Principal Component Analysis, are explored. Building on these, we introduce a comparative analysis of the machine learning and deep learning approaches in comparison to the NeSy. The experimental results show that the use of the NeSy approach matched strong MLP results despite a limited dataset. Future work on this research will expand the knowledge base, the scope of embedding types, and the hyperparameter configuration to further study the effectiveness of the NeSy approach.
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