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Analysing the history of autism spectrum disorder using topic models
conference contribution
posted on 2016-12-22, 00:00 authored by Adham Beykikhoshk, Quoc-Dinh Phung, O Arandjelovic, Svetha VenkateshSvetha VenkateshWe describe a novel framework for the discovery of underlying topics of a longitudinal collection of scholarly data, and the tracking of their lifetime and popularity over time. Unlike the social media or news data where the underlying topics evolve over time, the topic nuances in science result in new scientific directions to emerge. Therefore, we model the longitudinal literature data with a new approach that uses topics which remain identifiable over the course of time. Current studies either disregard the time dimension or treat it as an exchangeable covariate when they fix the topics over time or do not share the topics over epochs when they model the time naturally. We address these issues by adopting a non-parametric Bayesian approach. We assume the data is partially exchangeable and divide it into consecutive epochs. Then, by fixing the topics in a recurrent Chinese restaurant franchise, we impose a static topical structure on the corpus such that the topics are shared across epochs and the documents within epochs. We demonstrate the effectiveness of the proposed framework on a collection of medical literature related to autism spectrum disorder. We collect a large corpus of publications and carefully examine two important research issues of the domain as case studies. Moreover, we make the results of our experiment and the source code of the model, freely available to the public. This AIDS other researchers to analyse our results or apply the model to their data collections.
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Event
Data Science and Advanced Analytics. IEEE International Conference (3rd : 2016 : Montreal, Canada)Pagination
762 - 771Publisher
IEEELocation
Montreal, CanadaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2016-10-17End date
2016-10-19ISBN-13
9781509052066Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2016, IEEETitle of proceedings
DSAA 2016 : Proceedings of the 3rd IEEE International Conference on Data Science and Advanced AnalyticsUsage metrics
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