Balanced Korean word spacing with structural SVM

Abstract

Most studies on statistical Korean word spacing do not utilize the information provided by the input sentence and assume that it was completely concatenated. This makes the word spacer ignore the correct spaced parts of the input sentence and erroneously alter them. To overcome such limit, this paper proposes a structural SVM-based Korean word spacing method that can utilize the space information of the input sentence. The experiment on sentences with 10% spacing errors showed that our method achieved 96.81% F-score, while the basic structural SVM method only achieved 92.53% F-score. The more the input sentence was correctly spaced, the more accurately our method performed.