Distributional semantic models for the evaluation of disordered language
Distributional semantic models for the evaluation of disordered language
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发表时间：
2013
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通讯作者：
Masoud Rouhizadeh;Emily Tucker Prud'hommeaux;Brian Roark;J. Santen
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作者：
Masoud Rouhizadeh;Emily Tucker Prud'hommeaux;Brian Roark;J. Santen
自闭症儿童语言中经常出现非典型的语义和语用表达。虽然这种重复性经常表现在使用不寻常或意想不到的单词和短语，但这种意想不到的单词的使用率很少被直接测量或量化。在本文中，我们使用分布语义模型来自动识别自闭症儿童叙事复述中的意外单词。对意外词汇的分类足够准确，可以将自闭症儿童的复述与典型发育儿童的复述区分开来。这些技术证明了将自动语言分析技术应用于临床引发的语言数据用于诊断目的的潜力。
Atypical semantic and pragmatic expression is frequently reported in the language of children with autism. Although this atypicality often manifests itself in the use of unusual or unexpected words and phrases, the rate of use of such unexpected words is rarely directly measured or quantified. In this paper, we use distributional semantic models to automatically identify unexpected words in narrative retellings by children with autism. The classification of unexpected words is sufficiently accurate to distinguish the retellings of children with autism from those with typical development. These techniques demonstrate the potential of applying automated language analysis techniques to clinically elicited language data for diagnostic purposes.