Text Classification from Labeled and Unlabeled Documents using EM
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This study aims at identifying when an event written in text occurs. In particular, we classify a sentence for an event into four time-slots; morning, daytime, evening, and night. To realize our goal, we focus on expressions associated with time-slot (time-associated words). However, listing up all the time-associated words is impractical, because there are numerous time-associated expressions. We therefore use a semi-supervised learning method, the Naïve Bayes classifier backed up with the Expectation Maximization algorithm, in order to iteratively extract time-associated words while improving the classifier. We also propose to use Support Vector Machines to filter out noisy instances that indicates no specific time period. As a result of experiments, the proposed method achieved 0.864 of accuracy and outperformed other methods.