用词嵌入和机器学习对书籍摘要做分类，效果优于传统方法。
Categorical Classification of Book Summaries Using Word Embedding Techniques
- 对比TF-IDF、One-Hot、Word2Vec等词嵌入方法在分类中的表现
- TF-IDF与One-Hot结合支持向量机等模型在土耳其语上准确率更高
- 适合处理小语种文本分类任务的研究者参考
本研究利用词嵌入方法、自然语言处理技术及机器学习算法，对来自书评网站的书籍摘要及其类别进行分类。比较了常用词嵌入方法：独热编码（One-Hot Encoding）、Word2Vec以及词频-逆文档频率（TF-IDF）的性能，并展示了预处理方法组合表。实验结果表明，对于土耳其语文本，支持向量机（SVM）、朴素贝叶斯（Naive Bayes）与逻辑回归（Logistic Regression）模型，配合TF-IDF与独热编码，取得了更优分类效果。
原文摘要 · Abstract (English)
In this study, book summaries and categories taken from book sites were classified using word embedding methods, natural language processing techniques and machine learning algorithms. In addition, one hot encoding, Word2Vec and Term Frequency - Inverse Document Frequency (TF-IDF) methods, which are frequently used word embedding methods were used in this study and their success was compared. Additionally, the combination table of the pre-processing methods used is shown and added to the table. Looking at the results, it was observed that Support Vector Machine, Naive Bayes and Logistic Regression Models and TF-IDF and One-Hot Encoder word embedding techniques gave more successful results for Turkish texts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。