From Topic Models to Semi-Supervised Learning: Biasing Mixed-membership Models to Exploit Topic-Indicative Features in Entity Clustering Ramnath Balasubramanyan1 , Bhavana Dalvi1 , and William W. Cohen1,2 {rbalasub,bbd,w - Semi-membership - Document - PDFSEARCH.IO

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	From Topic Models to Semi-Supervised Learning: Biasing Mixed-membership Models to Exploit Topic-Indicative Features in Entity Clustering Ramnath Balasubramanyan1 , Bhavana Dalvi1 , and William W. Cohen1,2 {rbalasub,bbd,w Add to Reading List Document Date: 2013-07-01 09:45:45 Open Document File Size: 406,18 KB Share Result on Facebook Company vanilla LDA / underlying LDA / Amazon / Google / Delicious Music / Microsoft / / Continent ASIA / / / Facility Language Technologies Institute / / IndustryTerm finance / web domains / food / web tables / / NaturalFeature Shannon / / Organization Carnegie Mellon / Language Technologies Institute / Carnegie Mellon University1 Machine Learning Department / / Person Semi-Supervised Learning / / Position author / / Product Gibbs / / ProgrammingLanguage HTML / / Technology HTML / / SocialTag Topic model Artificial intelligence Dynamic topic model Statistical natural language processing Statistics Latent Dirichlet allocation

From Topic Models to Semi-Supervised Learning: Biasing Mixed-membership Models to Exploit Topic-Indicative Features in Entity Clustering Ramnath Balasubramanyan1 , Bhavana Dalvi1 , and William W. Cohen1,2 {rbalasub,bbd,wAdd to Reading List