Using Various Features in Machine Learning to Obtain High Levels of Performance for Recognition of Japanese Notational Variants

We proposed a method of using machine learning with various features for the recognition of Japanese notational variants. We increased 0.06 at the F-measure by specific features using existing dictionaries and character pairs useful for recognizing notational variants and obtained 0.91 at the F-measure for the recognition of notational variants. By using the method, we could extract 160 thousand word pairs with a precision rate of 0.9. We also constructed a method using patterns in addition to machine learning and observed that we could extract 4.2 million notational variant pairs with a precision rate of 0.78. We confirmed that our method was much better than an existing method through experiments.