Kernel- Based Chinese Recognition with Ontology
since the Chinese websites have increased in the explosive Internet era, making efficient information retrieval systems has become one of the major endeavors, especially in fields of Chinese recognition. In this paper, the authors study the integration of subsequence kernel function based on ontology. Using the Vector Space Model (VSM) to create subsequence kernels, the kernel methodology described here not only overcomes the VSM ignoring any semantic relation between words, but also results both in functional similarity and in sequence/words similarity by gap-weighted subsequences kernels, and the most important is that semantic character is also taken into account, which is very useful for Chinese recognition on internet. Experiments show that the method has more exact retrieval results, and its cost is under the accepted tolerance.
kernel ontology chinese recognition
Pang shuxia Li rui Yuan zhanting Zhang qiuyu
School of Computer and Communication Lanzhou University of Technology Lanzhou,China
国际会议
上海
英文
443-446
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）