As shown in the figure, the performances of using the three measures are very similar, which indicates that WMSEG is able to robustly incorporate the wordhood information from various measures, despite that those measures focus on different aspects of n-grams when determining whether the ngrams should be treated as words.
consider that the lexicons produced by the three measures are rather different in their sizes (as shown in Table 4), the results in Figure 2 strongly demonstrate the effectiveness of our proposed approach in learning with a limited number of n-grams.