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Comparison of Sentence-level Event Classification Algorithms  

Yun, Bo-Hyun (목원대학교 컴퓨터교육과)
Publication Information
The Journal of Korean Institute of Information Technology / v.10, no.6, 2012 , pp. 171-176 More about this Journal
Abstract
Conventional classification researches have been worked mostly on the document classification. However, because several topics exist within the document, setence-level classification is needed in order to extract only the topics and events. This paper compares SVM(Support Vector Machine), BN(Bayesian Network), and DT(Decision Tree) algorithms and presents a sentence-level event classification method to classify the event sentence from the news including the timeline information. Features are noun, noun phrase, verb, and named entities; the indexing uses the boolean weighting method. The filtering utilizes the information gain method. In the experimental result, the performance of linear kernel function shows better than that of others in the SVM kernel functions. SVM shows the best accuracy compared to BN and DT algorithms.
Keywords
event classification; machine learning; SVM;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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