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        <title>Analyzing Well-Formedness of Syllables in Japanese Sign Language</title>
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        <namePart type="given">Makoto</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Daisuke</namePart>
        <namePart type="family">Hara</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Analyzing Well-Formedness of Syllables in Japanese Sign Language
%A Yawata, Satoshi
%A Miwa, Makoto
%A Sasaki, Yutaka
%A Hara, Daisuke
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F yawata-etal-2017-analyzing
%X 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.
%U https://aclanthology.org/I17-2005
%P 26-30

Markdown (Informal)

[Analyzing Well-Formedness of Syllables in Japanese Sign Language](https://aclanthology.org/I17-2005) (Yawata et al., IJCNLP 2017)

• Analyzing Well-Formedness of Syllables in Japanese Sign Language (Yawata et al., IJCNLP 2017)

ACL

• Satoshi Yawata, Makoto Miwa, Yutaka Sasaki, and Daisuke Hara. 2017. Analyzing Well-Formedness of Syllables in Japanese Sign Language. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 26–30, Taipei, Taiwan. Asian Federation of Natural Language Processing.