An Improved Incremental Window Similarity Computing Method in Text Classification
This paper proposes an incremental window similarity computing method combined with ontology. In the paper, the classification is realized by changing the width of the window dynamically in the light of category tags provided by domain ontology, which avoids the influence of the window’s length on the similarity value. Through a set of experiments, the incremental window similarity computing method is proved to be superior to the other methods that work by avoiding the influence of the window’s length on the similarity value. The method in this paper, the traditional tf-idf, and Core Window-based Model similarity computing method is combined with the ontology to form the classifiers relatively. It is concluded that the incremental window similarity computing method has improved considerably in classification precision, recall, and F1_measure.
Incremental window Similarity computing Domain ontology Text classification
Yang Xiquan Jia Na Zou Caifeng Kuwatebaike Mamuti Tian Yukun
School of Computer Science and Information Technology Department of Computer Science and Technology School of Computer Science and Information Technology
国际会议
哈尔滨
英文
217-221
2011-01-18（万方平台首次上网日期，不代表论文的发表时间）