id
	15790
Url
	https://chloe.cnr.it/s/BiDiAr/item/15790
Resource template
	Conference Paper
Resource class
	fabio:ConferencePaper
Title
	Finding scientific topics
Creator
	Griffiths, Thomas L.
	Steyvers, Mark
Date
	2004
Language
	eng
Abstract
	A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003)               J. Machine Learn. Res.               3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying “hot topics” by examining temporal dynamics and tagging abstracts to illustrate semantic content.
Is Part Of
	Proceedings of the National Academy of Sciences
Doi
	https://doi.org/10.1073/pnas.0307752101
Pages
	5228-5235
Volume
	101
Homepage
	https://www.zotero.org/groups/5293298/bidiar/items/RPWTERTZ/item-list

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