Tibetan Word Segmentation as Sub-syllable Tagging with Syllable's Part-of-Speech Property

When Tibetan word segmentation task is taken as a sequence labelling problem, machine learning models such as ME and CRFs can be used to train the segmenter. The performance of the segmenter is related to many factors. In the paper, three factors, namely strategy on abbreviated syllables, tag set, and the syllable’s Part-Of-Speech property, are compared. Experiment data show that: first, if each abbreviate syllable is separated into two units for labelling rather than one, the F-measure improves 0.06% and 0.10% on 4-tag set and 6-tag set respectively. Second, if 6-tag set is used rather than 4-tag set, the F-measure improves 0.10% and 0.14% on the two strategies on abbreviated syllables respectively. Third, when the syllable’s Part-Of-Speech property is take into account, F-measure improves 0.47% and 0.41% respectively than the other two methods without using it on 4-tag set, while it improves 0.45% and 0.35% on 6-tag set, which is much more higher than the former improvements. So it’s a better choice to take advantage of the syllable’s Part-Of-Speech property information while using the sub-syllable as the tag unit.