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Detecting linguistic idiosyncratic interests in autism using distributional semantic models

机译:使用分配语义模型检测自闭症中的语言特质兴趣

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Children with autism spectrum disorder often exhibit idiosyncratic patterns of behaviors and interests. In this paper, we focus on measuring the presence of idiosyncratic interests at the linguistic level in children with autism using distributional semantic models. We model the semantic space of children's narratives by calculating pairwise word overlap, and we compare the overlap found within and across diagnostic groups. We find that the words used by children with typical development tend to be used by other children with typical development, while the words used by children with autism overlap less with those used by children with typical development and even less with those used by other children with autism. These findings suggest that children with autism are veering not only away from the topic of the target narrative but also in idiosyncratic semantic directions potentially defined by their individual topics of interest.
机译:具有自闭症谱系障碍的儿童通常表现出特质的行为和兴趣。在本文中,我们专注于使用分布语义模型的自闭症儿童的语言水平来测量具有特殊性兴趣的存在。我们通过计算成对字重叠来模拟儿童叙述的语义空间,我们比较诊断组内部和跨诊断组中的重叠。我们发现,典型发展的儿童使用的单词往往被具有典型发展的其他儿童使用,而自闭症儿童使用的单词与典型发展的儿童使用的人重叠较少,而其他儿童使用的孩子甚至少自闭症。这些调查结果表明,患有自闭症的儿童不仅从目标叙述的主题转向,而且在潜在的兴趣主题所定义的特殊语义方向上。

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