@InProceedings{yawata-EtAl:2017:I17-2,
  author    = {Yawata, Satoshi  and  Miwa, Makoto  and  Sasaki, Yutaka  and  Hara, Daisuke},
  title     = {Analyzing Well-Formedness of Syllables in Japanese Sign Language},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {26--30},
  abstract  = {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 well-formed or ill-formed. We build a
	data set that contains hand-coded syllables and their well-formedness. We
	define a fine-grained feature set based on the hand-coded syllables and train a
	logistic regression classifier on labeled syllables, expecting to find the
	discriminative features from the trained classifier. 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.},
  url       = {http://www.aclweb.org/anthology/I17-2005}
}