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Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by im- paired social communication, and restricted, repetitive patterns of behavior and interest. These two core symptoms can appear at the language level and result in problems such as using inappropriate words, idiosyncratic language, topic repetition, and lack of conversa- tional responsiveness. Existing methods for the evaluation of language in ASD are mainly based on subjective parental and clinical reports. In this thesis, we propose fast, objec- tive, scalable, automatic analysis of these interrelated aspects of ASD language, utilizing computational methods for natural language processing based on unannotated verbatim transcripts of conversations.