@inproceedings{conf/icdm/AlSumaitBD08, author = {AlSumait, Loulwah and Barbará, Daniel and Domeniconi, Carlotta}, booktitle = {ICDM}, crossref = {conf/icdm/2008}, date = {2009-02-20}, ee = {http://dx.doi.org/10.1109/ICDM.2008.140}, interhash = {e46dde3d53c823afeeb7604f1991b661}, intrahash = {980e5cf0b3db547cf47c2c203734ac83}, pages = {3-12}, publisher = {IEEE Computer Society}, title = {On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking.}, url = {http://dblp.uni-trier.de/db/conf/icdm/icdm2008.html#AlSumaitBD08}, year = 2008 } @article{pilz-named, author = {Pilz, A. and Paa{\ss}, G. and Augustin, G. St}, interhash = {056d7a3a9a77c31044e996acfb23cc8c}, intrahash = {f956b02691b503629c6742b3e61489da}, title = {{Named Entity Resolution Using Automatically Extracted Semantic Information}}, url = {http://scholar.google.de/scholar.bib?q=info:3tHCLbaX3_sJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0}, year = 2009 } @article{journals/jmlr/BanerjeeMDG05, author = {Banerjee, Arindam and Merugu, Srujana and Dhillon, Inderjit S. and Ghosh, Joydeep}, date = {2007-02-21}, ee = {http://www.jmlr.org/papers/v6/banerjee05b.html}, interhash = {50d46127d134382ca84699ce24171c3f}, intrahash = {bba5d5241acf3ec9eea3f869a832c629}, journal = {Journal of Machine Learning Research}, pages = {1705-1749}, title = {Clustering with Bregman Divergences.}, url = {http://dblp.uni-trier.de/db/journals/jmlr/jmlr6.html#BanerjeeMDG05}, volume = 6, year = 2005 } @article{Dempster77maximumlikelihood, abstract = {A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis. }, author = {Dempster, A. P. and Laird, N. M. and Rubin, D. B.}, interhash = {6a3c3e7e36b05f7855a57eab65f93593}, intrahash = {c0eee49f4f3379bac851721935e5141a}, journal = {JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B}, number = 1, pages = {1--38}, title = {Maximum likelihood from incomplete data via the EM algorithm}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.133.4884}, volume = 39, year = 1977 } @article{nallapati2008link, author = {Nallapati, R. and Cohen, W.}, booktitle = {International Conference for Weblogs and Social Media}, interhash = {fc5e49ebae9358381b43981b6794158b}, intrahash = {a1f50ddb9b2734e224d4c4ea0140c7cd}, title = {{Link-PLSA-LDA: A new unsupervised model for topics and influence of blogs}}, url = {http://scholar.google.de/scholar.bib?q=info:WFXUMUlnnKwJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0}, year = 2008 } @article{bishop1998latent, author = {Bishop, C.M.}, interhash = {3d556e46becabe2ec132942af1409079}, intrahash = {8c95ee19fc4410c654b624cb9bd6ff57}, journal = {Learning in graphical models}, title = {{Latent variable models}}, url = {http://scholar.google.de/scholar.bib?q=info:fwgN--1AHXsJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0}, year = 1998 } @article{339252, address = {Hingham, MA, USA}, author = {Jordan, Michael I. and Ghahramani, Zoubin and Jaakkola, Tommi S. and Saul, Lawrence K.}, doi = {http://dx.doi.org/10.1023/A:1007665907178}, interhash = {f305ddebfd438a2575f09d72467a81c9}, intrahash = {30a0c13528ae353d38e6c8aed9db7821}, issn = {0885-6125}, journal = {Mach. Learn.}, number = 2, pages = {183--233}, publisher = {Kluwer Academic Publishers}, title = {An Introduction to Variational Methods for Graphical Models}, url = {http://portal.acm.org/citation.cfm?id=339248.339252}, volume = 37, year = 1999 } @inbook{steyvers_griffiths07, author = {Steyvers, M. and Griffiths, T.}, chapter = {Probabilistic topic models}, editor = {Landauer, T. and McNamara, S. Dennis and Kintsch, W.}, interhash = {4b2ab9b5eecc61ba49087d932b25f11c}, intrahash = {ab33268b60e8774bdfe46cd50a970fa3}, owner = {gregor}, publisher = {Laurence Erlbaum}, timestamp = {2008.02.26}, title = {Latent Semantic Analysis: A Road to Meaning}, year = 2007 } @article{mccallum2007topic, author = {McCallum, A. and Wang, X. and Corrada-Emmanuel, A.}, interhash = {56511b795458d88811bffd6ad8ec1e89}, intrahash = {e7a6d3c9bd46ddc77a62e04d35aff330}, journal = {Journal of Artificial Intelligence Research}, pages = {249--272}, title = {{Topic and role discovery in social networks with experiments on enron and academic email}}, url = {http://scholar.google.de/scholar.bib?q=info:GVi_TXpyWz8J:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0}, volume = 30, year = 2007 } @inproceedings{shafiei_milios06, address = {Washington, DC, USA}, author = {Shafiei, M. Mahdi and Milios, Evangelos E.}, bdsk-url-1 = {http://dx.doi.org/10.1109/ICDM.2006.94}, booktitle = {ICDM '06: Proceedings of the Sixth International Conference on Data Mining}, doi = {http://dx.doi.org/10.1109/ICDM.2006.94}, interhash = {5850c178c4393ec9c7ea8b0f7f9271c5}, intrahash = {f36c376d49e03acd1cae83c758327943}, isbn = {0-7695-2701-9}, owner = {gregor}, pages = {542--551}, publisher = {IEEE Computer Society}, timestamp = {2008.04.25}, title = {Latent {D}irichlet Co-Clustering}, year = 2006 } @article{gregor2009generic, abstract = {This article contributes a generic model of topic models. To define the problem space, general characteristics for this class of models are derived, which give rise to a representation of topic models as “mixture networks”, a domain-specific compactalternative to Bayesian networks. Besides illustrating the interconnection of mixtures in topic models, the benefit of thisrepresentation is its straight-forward mapping to inference equations and algorithms, which is shown with the derivation andimplementation of a generic Gibbs sampling algorithm.}, author = {Heinrich, Gregor}, interhash = {9509ac0016837f04415cfcb5ef2ea93c}, intrahash = {dd184722f6239798835404daa73f9d36}, journal = {Machine Learning and Knowledge Discovery in Databases}, pages = {517--532}, title = {A Generic Approach to Topic Models}, url = {http://dx.doi.org/10.1007/978-3-642-04180-8_51}, year = 2009 } @incollection{griffiths_05, address = {Cambridge, MA}, author = {{Griffiths}, Thomas L. and {Steyvers}, Mark and {Blei}, David M. and {Tenenbaum}, Joshua B.}, booktitle = {Advances in Neural Information Processing Systems 17}, editor = {Saul, Lawrence K. and Weiss, Yair and Bottou, {L\'{e}on}}, interhash = {7d2594aa4b9905370ef001eebb6461b7}, intrahash = {dd89145403b46fc5315d3206f89bb09b}, owner = {heinrich}, pages = {537-544}, publisher = {MIT Press}, title = {Integrating Topics and Syntax}, volume = 17, year = 2005 } @article{Buntine94operationsfor, abstract = {This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...}, author = {Buntine, Wray L.}, interhash = {c7dd650780467c934551356630a7b739}, intrahash = {8952cf0d215116e038971f7c30d6d19d}, journal = {Journal of Artificial Intelligence Research}, pages = {159--225}, title = {Operations for Learning with Graphical Models}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.696}, volume = 2, year = 1994 } @article{buntine2002variational, author = {Buntine, W.}, interhash = {12e54792e55b4f6ec694ae7b2af352f6}, intrahash = {e8920842d95355c880fd8b9dafbf4fc1}, journal = {Lecture notes in computer science}, pages = {23--34}, publisher = {Springer}, title = {{Variational extensions to EM and multinomial PCA}}, url = {http://scholar.google.de/scholar.bib?q=info:2EiODWPLMzEJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0}, year = 2002 } @article{griffiths_steyvers04, author = {Griffiths, T. L. and Steyvers, M.}, interhash = {387a5060792d52ea73b02dd68e52559e}, intrahash = {49705b433fcb5b87c0a3a140c40f9a4d}, journal = {Proceedings of the National Academy of Sciences}, month = {April}, number = {Suppl. 1}, owner = {heinrich}, pages = {5228-5235}, title = {Finding scientific topics}, volume = 101, year = 2004 } @misc{murphy01, author = {Murphy, Kevin}, bdsk-url-1 = {http://www.ai.mit.edu/~murphyk/Papers/intro_gm.pdf}, howpublished = {Web}, interhash = {d1fcd0ead413e934c318979cf4285b72}, intrahash = {b3dfa03c046b5ea0a6790f986abc03f9}, owner = {heinrich}, timestamp = {2009.04.07}, title = {An introduction to graphical models}, url = {http://www.ai.mit.edu/~murphyk/Papers/intro_gm.pdf}, year = 2001 } @article{jordan2002probabilistic, author = {Jordan, M.I. and Weiss, Y.}, interhash = {16441ec7a8c3d1c6e4fc04e4047f37fd}, intrahash = {8d808e345b50efd5eb2fd82c8c8f4b2c}, journal = {Handbook of neural networks and brain theory}, publisher = {Citeseer}, title = {{Probabilistic inference in graphical models}}, url = {http://scholar.google.de/scholar.bib?q=info:gt-LduJsWooJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0}, year = 2002 } @techreport{jordan2003, author = {Jordan, Michael I.}, interhash = {979f98f68a4b6057a4242f7d432c6762}, intrahash = {a71abfadd0bb0e2f0e0ff8f6b98c27b6}, title = {Learning in graphical models}, year = 2003 } @book{jordan1998learning, author = {Jordan, M.I.}, interhash = {dca14c475ead34e75711dfe8bb911d96}, intrahash = {9e4542bbc55ee07b8fa1c45d465b2f95}, publisher = {Kluwer Academic Publishers}, title = {{Learning in graphical models}}, url = {http://scholar.google.de/scholar.bib?q=info:EZqYGcIKUI8J:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0}, year = 1998 } @article{asuncion, author = {Asuncion, A. and Welling, M. and Smyth, P. and Teh, Y.W.}, interhash = {8e02687513e37ddc5fe2a26532fd5651}, intrahash = {d379225abf14a84f3e35b255cfaf1f42}, title = {{On Smoothing and Inference for Topic Models}}, url = {http://scholar.google.de/scholar.bib?q=info:lTdYvJVnpNwJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0}, year = 2009 }