Finding scientific topics Why map knowledge Why map knowledge INFORMATION OVERLOAD

Finding scientific topics Why map knowledge Why map knowledge INFORMATION OVERLOAD www.phwiki.com

Finding scientific topics Why map knowledge Why map knowledge INFORMATION OVERLOAD

Danehy, Tom, Host has reference to this Academic Journal, PHwiki organized this Journal Finding scientific topics Tom Griffiths Stan as long as d University Mark Steyvers UC Irvine Why map knowledge Quickly grasp important themes in a new field Synthesize content of an existing field Discover targets as long as funding in addition to research Why map knowledge Quickly grasp important themes in a new field Synthesize content of an existing field Discover targets as long as funding in addition to research INFORMATION OVERLOAD

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Apoptosis + Plant Biology Apoptosis + Medicine Apoptosis + Medicine

Apoptosis + Medicine Apoptosis + Medicine Apoptosis + Medicine probabilistic generative model Apoptosis + Medicine

statistical inference Apoptosis + Medicine 1. A generative model as long as documents 2. Discovering topics with Gibbs sampling 3. Results Topics in addition to classes Mapping science Topic dynamics 4. Future directions Tagging abstracts 1. A generative model as long as documents 2. Discovering topics with Gibbs sampling 3. Results Topics in addition to classes Mapping science Topic dynamics 4. Future directions Tagging abstracts

A generative model as long as documents Each document a mixture of topics Each word chosen from a single topic from parameters from parameters (Blei, Ng, & Jordan, 2003) A generative model as long as documents HEART 0.2 LOVE 0.2 SOUL 0.2 TEARS 0.2 JOY 0.2 SCIENTIFIC 0.0 KNOWLEDGE 0.0 WORK 0.0 RESEARCH 0.0 MATHEMATICS 0.0 HEART 0.0 LOVE 0.0 SOUL 0.0 TEARS 0.0 JOY 0.0 SCIENTIFIC 0.2 KNOWLEDGE 0.2 WORK 0.2 RESEARCH 0.2 MATHEMATICS 0.2 topic 1 topic 2 w P(wz = 1) = f (1) w P(wz = 2) = f (2) Choose mixture weights as long as each document, generate “bag of words” q = {P(z = 1), P(z = 2)} {0, 1} {0.25, 0.75} {0.5, 0.5} {0.75, 0.25} {1, 0} MATHEMATICS KNOWLEDGE RESEARCH WORK MATHEMATICS RESEARCH WORK SCIENTIFIC MATHEMATICS WORK SCIENTIFIC KNOWLEDGE MATHEMATICS SCIENTIFIC HEART LOVE TEARS KNOWLEDGE HEART MATHEMATICS HEART RESEARCH LOVE MATHEMATICS WORK TEARS SOUL KNOWLEDGE HEART WORK JOY SOUL TEARS MATHEMATICS TEARS LOVE LOVE LOVE SOUL TEARS LOVE JOY SOUL LOVE TEARS SOUL SOUL TEARS JOY

A generative model as long as documents Called Latent Dirichlet Allocation (LDA) Introduced by Blei, Ng, in addition to Jordan (2003), reinterpretation of PLSI (Hofmann, 2001) q z w z z w w P(w) (Dumais, L in addition to auer) 1. A generative model as long as documents 2. Discovering topics with Gibbs sampling 3. Results Topics in addition to classes Mapping science Topic dynamics 4. Future directions Tagging abstracts

Inverting the generative model Maximum likelihood estimation (EM) Variational EM (Blei, Ng & Jordan, 2003) Bayesian inference Bayesian inference Sum in the denominator over Tn terms Full posterior only tractable to a constant Markov chain Monte Carlo Sample from a Markov chain which converges to target distribution Allows sampling from an unnormalized posterior distribution Can compute approximate statistics from intractable distributions

A visual example: Bars pixel = word image = document sample each pixel from a mixture of topics

Danehy, Tom Inside Track - KJLL-AM Host www.phwiki.com

Interpretable decomposition SVD gives a basis as long as the data, but not an interpretable one The true basis is not orthogonal, so rotation does no good Bayesian model selection How many topics do we need A Bayesian would consider the posterior: Involves summing over assignments z P(Tw) P(wT) P(T) Bayesian model selection Corpus (w) P( w T ) T = 10 T = 100

Bayesian model selection Corpus (w) P( w T ) T = 10 T = 100 Bayesian model selection Corpus (w) P( w T ) T = 10 T = 100 Back to the bars

Gibbs sampling iteration 1 2 Gibbs sampling iteration 1 2 Gibbs sampling iteration 1 2 1000

Danehy, Tom Host

Danehy, Tom is from United States and they belong to Inside Track – KJLL-AM and they are from  Tucson, United States got related to this Particular Journal. and Danehy, Tom deal with the subjects like Federal Government and Politics

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