This paper tackles a problem of analyzing the well-formedness of syllables in Japanese Sign Language (JSL). We formulate the problem as a classification problem that classifies syllables into wellformed or ill-formed. We build a data set that contains hand-coded syllables and their well-formedness. We define a finegrained feature set based on the handcoded syllables and train a logistic regression classifier on labeled syllables, expecting to find the discriminative features from the trained

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... ssifier. We also perform pseudo active learning to investigate the applicability of active learning in analyzing syllables. In the experiments, the best classifier with our combinatorial features achieved the accuracy of 87.0%. The pseudo active learning is also shown to be effective showing that it could reduce about 84% of training instances to achieve the accuracy of 82.0% when compared to the model without active learning.