Using Latent Dirichlet Allocation for Child Narrative Analysis Khairun-nisa Hassanali 1, Yang Liu 1 and Thamar Solorio 2 1 The University of Texas at Dallas 2 University of Alabama at Birmingham 1. Summary  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 7. Conclusion 3. Data  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  Identified topics corresponding to narrative structure  Identified subtopics 2. Introduction  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?  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 5. U sing LDA Topic Related Features For Detection of LI and Coherence No Topic Words Used by TD PopulationTopic Described 1 went, frog, sleep, glass, put, caught, jar, yesterday, out, house Introduction 2 frog, up, woke, morning, called, gone, escaped, next, kept, realized Frog goes missing 3 window, out, fell, dog, falls, broke, quickly, opened, told, breaking Dog falls out of the window 4 tree, bees, knocked, running, popped, chase, dog, inside, now, flying Dog chases the bees 5 deer, rock, top, onto, sort, big, up, behind, rocks, picked Deer behind the rock 6 searched, boots, room, bedroom, under, billy, even, floor, tilly, tried Search for frog in the room 7 dog, chased, owl, tree, bees, boy, came, hole, up, more Boy is chased by owl from a tree with beehives 8 jar, gone, woke, escaped, night, sleep, asleep, dressed, morning, frog Frog goes missing 9 deer, top, onto, running, ways, up, rocks, popped, suddenly, know Boy runs into the deer 10 looking, still, dog, quite, cross, obviously, smashes, have, annoyed Displeasure of boy with dog BIONLP Experiments 4. Topic Words Extracted by LDA  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 Features PrecisionRecall F-1 Gabani et al.’s (2011) (baseline) Narrative (Hassanali et al., 2012a) Topic features Narrative + Gabani’s Narrative + Gabani’s + topic features  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 Feature CoherentIncoherent PRF-1PR Narrative (baseline) (Hassanali et al) Narrative + automatic topic Features Automatic Prediction of LI Automatic Prediction of Coherence This research is sponsored by  Used LDA to generate topic words K= 20, alpha = 0.8  Used transcripts of TD children