  In this paper, we approach Vietnamese word segmentation as a binary
classification by using the Support Vector Machine classifier. We inherit
features from prior works such as n-gram of syllables, n-gram of syllable
types, and checking conjunction of adjacent syllables in the dictionary. We
propose two novel ways to feature extraction, one to reduce the overlap
ambiguity and the other to increase the ability to predict unknown words
containing suffixes. Different from UETsegmenter and RDRsegmenter, two
state-of-the-art Vietnamese word segmentation methods, we do not employ the
longest matching algorithm as an initial processing step or any post-processing
technique. According to experimental results on benchmark Vietnamese datasets,
our proposed method obtained a better F1-score than the prior state-of-the-art
methods UETsegmenter, and RDRsegmenter.
