Using Latent Dirichlet Allocation for Child Narrative Analysis

1. 5. Using LDA Topic Related Features For Detection of LI and Coherence 7. Conclusion 3. Data 4. Topic Words Extracted by LDA 1. Summary 2. Introduction 6. Experiments Using Latent Dirichlet Allocation for Child Narrative Analysis BIONLP 2013 • Automatic classification of LI and coherence • Naïve Bayes classifier performed the best • Leave one out cross validation • Use of topic based features, in addition to baseline features, led to improved performance for both tasks • Used LDA to generate topic words K= 20, alpha = 0.8 • Used transcripts of TD children • We explore the use of Latent Dirichlet Allocation (LDA) for detecting topics from child narratives • We use LDA topics in two classification tasks: • Automatic prediction of Language Impairment (LI) • Automatic prediction of coherence • Findings: • LDA is useful for detecting topics that correspond to the narrative structure • Improved performance in the automatic prediction of LI and coherence Khairun-nisa Hassanali1, Yang Liu1 and Thamar Solorio2 nisa@hlt.utdallas.eduyangl@hlt.utdallas.edusolorio@uab.edu 1The University of Texas at Dallas 2University of Alabama at Birmingham Automatic Prediction of LI • Child language narratives are used for language analysis, measurement of language development, and the detection of LI • Automatic detection of LI is faster and allows for exploring more features beyond norm referenced tests • Given a child language transcript, answer the following question: • Does the transcript belong to a typically developing (TD) child or a child with LI? • Is the narrative produced by the child understandable or coherent? Automatic Prediction of Coherence • Identified topics corresponding to narrative structure • Identified subtopics • Used LDA topics to generate a summary and extended vocabulary • Used extended vocabulary to detect presence or absence of topics • Automatic classification of LI • Count of bigrams of the words in the summary • Presence or absence of LDA topic keywords • Presence or absence of words in the summary • Automatic classification of coherence • Presence or absence of LDA topics • Explored the use of LDA in the context of child language analysis • Used LDA topics from child narratives to create an extended vocabulary and summary • The LDA topic keywords covered the main components of the narrative • Improved performance in the automatic prediction of LI and coherence when using LDA topic keywords to create features, in addition to baseline features • Transcripts of adolescents aged 14 years • Story telling task based on the picture book “Frog, where are you?” • 118 speakers (99 TD children, 18 LI children) • 118 transcripts (87 coherent, 31 incoherent) • Transcripts are annotated for language impairment and coherence This research is sponsored by