比较SVM和朴素贝叶斯分类器在Wikitology作为知识丰富的文本分类中的应用
Comparing SVM and Naive Bayes classifiers for text categorization with Wikitology as knowledge enrichment
摘要：
根据文档内容对文档进行标记的活动称为文本分类。已经进行了许多实验，以提高文本分类，通过添加背景知识的文件使用知识库，如Word Net，开放项目目录（OPD），维基百科和Wikitology。在我们以前的工作中，我们已经进行了深入的实验，从Wikitology中提取知识，并评估实验支持向量机与10倍交叉验证。结果清楚地表明，Wikitology远远优于其他知识库。在本文中，我们比较支持向量机（SVM）和朴素贝叶斯（NB）分类器下的文本丰富通过维基百科。我们用10倍交叉验证验证了结果，结果表明，与基线结果相比，NB提高了+28.78%，另一方面，SVM提高了+6.36%。当通过任何外部知识库使用外部丰富时，朴素贝叶斯分类器是更好的选择。
英文摘要：
The activity of labeling of documents according to their content is known as text categorization. Many experiments have been carried out to enhance text categorization by adding background knowledge to the document using knowledge repositories like Word Net, Open Project Directory (OPD), Wikipedia and Wikitology. In our previous work, we have carried out intensive experiments by extracting knowledge from Wikitology and evaluating the experiment on Support Vector Machine with 10- fold cross-validations. The results clearly indicate Wikitology is far better than other knowledge bases. In this paper we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers under text enrichment through Wikitology. We validated results with 10-fold cross validation and shown that NB gives an improvement of +28.78%, on the other hand SVM gives an improvement of +6.36% when compared with baseline results. Naïve Bayes classifier is better choice when external enriching is used through any external knowledge base.
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。
