Using Various Features in Machine Learning to Obtain High Levels of Performance for Recognition of Japanese Notational Variants
Masahiro Kojima, Masaki Murata, Jun’ichi Kazama, Kow Kuroda, Atsushi Fujita, Eiji Aramaki, Masaaki Tsuchida, Yasuhiko Watanabe, Kentaro Torisawa · Institutional Repositories DataBase (IRDB) · 2010
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.