Detection of Peculiar Examples using LOF and One Class SVM

This paper proposes the method to detect peculiar examples of the target word from a corpus. The peculiar example is regarded as an outlier in the given example set. Therefore we can apply many methods proposed in the data mining domain to our task. In this paper, we propose the method to combine the density based method, Local Outlier Factor (LOF), and One Class SVM, which are representative outlier detection methods in the data mining domain. In the experiment, we use the Whitepaper text in BCCWJ as the corpus, and 10 noun words as target words. Our method improved precision and recall of LOF and One Class SVM. And we show that our method can detect new meanings by using the noun `midori (green)'. The main reason of un-detections and wrong detection is that similarity measure of two examples is inadequacy. In future, we must improve it.

• Type: Conference
• Year: 2010
• Where: LREC
• Authors: Hiroyuki Shinnou, Minoru Sasaki
• Keywords: Data Mining Domain, Education, LREC 2010, Outlier, Peculiar Example