Using Latent Dirichlet Allocation for Child Narrative Analysis

Child language narratives are used for language analysis, measurement of language development, and the detection of language impairment. In this paper, we explore the use of Latent Dirichlet Allocation (LDA) for detecting topics from narratives, and use the topics derived from LDA in two classification tasks: automatic prediction of coherence and language impairment. Our experiments show LDA is useful for detecting the topics that correspond to the narrative structure. We also observed improved performance for the automatic prediction of coherence and language impairment when we use features derived from the topic words provided by LDA.