Supervised topic models with word order structure for document classification and retrieval learning

[1]  Shan Suthaharan,et al.  Support Vector Machine , 2016 .

[2]  Lidong Bing,et al.  Nonparametric Topic Modeling Using Chinese Restaurant Franchise with Buddy Customers , 2015, ECIR.

[3]  Jihong Ouyang,et al.  Supervised topic models for multi-label classification , 2015, Neurocomputing.

[4]  Andrew M. Dai,et al.  The Supervised Hierarchical Dirichlet Process , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Zhou Yu,et al.  Hashing with List-Wise learning to rank , 2014, SIGIR.

[6]  Xueqi Cheng,et al.  What makes data robust: a data analysis in learning to rank , 2014, SIGIR.

[7]  Hang Li,et al.  Semantic Matching in Search , 2014, SMIR@SIGIR.

[8]  Marco Masseroli,et al.  Latent Dirichlet Allocation based on Gibbs Sampling for gene function prediction , 2014, 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology.

[9]  Jimmy J. Lin,et al.  Learning to efficiently rank on big data , 2014, WWW.

[10]  Philipp Koehn,et al.  Dynamic Topic Adaptation for Phrase-based MT , 2014, EACL.

[11]  Noriaki Kawamae,et al.  Supervised N-gram topic model , 2014, WSDM.

[12]  Renjie Liao,et al.  Nonparametric bayesian upstream supervised multi-modal topic models , 2014, WSDM.

[13]  Wei Gao,et al.  Democracy is good for ranking: towards multi-view rank learning and adaptation in web search , 2014, WSDM.

[14]  Wai Lam,et al.  A Nonparametric N-Gram Topic Model with Interpretable Latent Topics , 2013, AIRS.

[15]  Hedvig Kjellström,et al.  Supervised Hierarchical Dirichlet Processes with Variational Inference , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[16]  Balázs Kégl,et al.  Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers , 2013, Machine Learning.

[17]  Jian-Yun Nie,et al.  Modeling latent topic interactions using quantum interference for information retrieval , 2013, CIKM.

[18]  Bo Zhang,et al.  Improved Bayesian Logistic Supervised Topic Models with Data Augmentation , 2013, ACL.

[19]  Nicholas Jing Yuan,et al.  We know how you live: exploring the spectrum of urban lifestyles , 2013, COSN '13.

[20]  Yusuke Miyao,et al.  Improvements to the Bayesian Topic N-Gram Models , 2013, EMNLP.

[21]  Raymond J. Mooney,et al.  Using Both Latent and Supervised Shared Topics for Multitask Learning , 2013, ECML/PKDD.

[22]  Bo Zhang,et al.  Scalable inference in max-margin topic models , 2013, KDD.

[23]  Tian Xia,et al.  Direct optimization of ranking measures for learning to rank models , 2013, KDD.

[24]  Wai Lam,et al.  An unsupervised topic segmentation model incorporating word order , 2013, SIGIR.

[25]  Jimmy J. Lin,et al.  Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures , 2013, SIGIR.

[26]  Jianfeng Gao,et al.  Modeling click-through based word-pairs for web search , 2013, SIGIR.

[27]  Ning Chen,et al.  Gibbs Max-Margin Topic Models with Fast Sampling Algorithms , 2013, ICML.

[28]  Jie Wu,et al.  Sparse Learning-to-Rank via an Efficient Primal-Dual Algorithm , 2013, IEEE Transactions on Computers.

[29]  Wensheng Wu,et al.  Searching the deep web using proactive phrase queries , 2013, WWW.

[30]  Xiaohui Yan,et al.  A biterm topic model for short texts , 2013, WWW.

[31]  Wai Lam,et al.  An N-Gram Topic Model for Time-Stamped Documents , 2013, ECIR.

[32]  W. Bruce Croft,et al.  Two-Stage Learning to Rank for Information Retrieval , 2013, ECIR.

[33]  G. Huang,et al.  Learning to Rank with Extreme Learning Machine , 2013, Neural Processing Letters.

[34]  Ruixuan Li,et al.  RankTopic: Ranking Based Topic Modeling , 2012, 2012 IEEE 12th International Conference on Data Mining.

[35]  Maosong Sun,et al.  Monte Carlo Methods for Maximum Margin Supervised Topic Models , 2012, NIPS.

[36]  Ning Chen,et al.  Bayesian inference with posterior regularization and applications to infinite latent SVMs , 2012, J. Mach. Learn. Res..

[37]  Ning Chen,et al.  Bayesian Inference with Posterior Regularization and Infinite Latent Support Vector Machines , 2012, ArXiv.

[38]  Rodrygo L. T. Santos,et al.  The whens and hows of learning to rank for web search , 2012, Information Retrieval.

[39]  Yizhou Sun,et al.  Probabilistic Models for Text Mining , 2012, Mining Text Data.

[40]  Robert V. Lindsey,et al.  A Phrase-Discovering Topic Model Using Hierarchical Pitman-Yor Processes , 2012, EMNLP.

[41]  Rebecca J. Passonneau,et al.  Supervised HDP Using Prior Knowledge , 2012, NLDB.

[42]  Frank D. Wood,et al.  Hierarchically Supervised Latent Dirichlet Allocation , 2011, NIPS.

[43]  Ning Chen,et al.  Infinite Latent SVM for Classification and Multi-task Learning , 2011, NIPS.

[44]  Raviv Raich,et al.  Inference in Supervised latent Dirichlet allocation , 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

[45]  Yelong Shen,et al.  Learning to rank audience for behavioral targeting in display ads , 2011, CIKM '11.

[46]  Hang Li,et al.  A Short Introduction to Learning to Rank , 2011, IEICE Trans. Inf. Syst..

[47]  David Buttler,et al.  Latent topic feedback for information retrieval , 2011, KDD.

[48]  Wei Gao,et al.  Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation , 2011, SIGIR.

[49]  Quan Wang,et al.  Regularized latent semantic indexing , 2011, SIGIR.

[50]  Jianfeng Gao,et al.  Clickthrough-based latent semantic models for web search , 2011, SIGIR.

[51]  Timothy N. Rubin,et al.  Statistical topic models for multi-label document classification , 2011, Machine Learning.

[52]  Christopher Potts,et al.  Learning Word Vectors for Sentiment Analysis , 2011, ACL.

[53]  Evgeniy Gabrilovich,et al.  Concept-Based Information Retrieval Using Explicit Semantic Analysis , 2011, TOIS.

[54]  Yue Lu,et al.  Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA , 2011, Information Retrieval.

[55]  Timothy J. Hazen.  Direct and latent modeling techniques for computing spoken document similarity , 2010, 2010 IEEE Spoken Language Technology Workshop.

[56]  David B. Dunson,et al.  Probabilistic topic models , 2012, Commun. ACM.

[57]  Tao Qin,et al.  LETOR: A benchmark collection for research on learning to rank for information retrieval , 2010, Information Retrieval.

[58]  David Pfau,et al.  Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process , 2010, ICML.

[59]  Qiang Wu,et al.  Adapting boosting for information retrieval measures , 2010, Information Retrieval.

[60]  Yanjun Qi,et al.  Learning to rank with (a lot of) word features , 2010, Information Retrieval.

[61]  Alessandro Perina,et al.  Expression microarray classification using topic models , 2010, SAC '10.

[62]  Ben Taskar,et al.  Posterior Regularization for Structured Latent Variable Models , 2010, J. Mach. Learn. Res..

[63]  Andrew McCallum,et al.  Rethinking LDA: Why Priors Matter , 2009, NIPS.

[64]  Kotagiri Ramamohanarao,et al.  The Sensitivity of Latent Dirichlet Allocation for Information Retrieval , 2009, ECML/PKDD.

[65]  Ramesh Nallapati,et al.  Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora , 2009, EMNLP.

[66]  Andrew McCallum,et al.  Efficient methods for topic model inference on streaming document collections , 2009, KDD.

[67]  Chong Wang,et al.  Simultaneous image classification and annotation , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[68]  Mehran Sahami,et al.  Text Mining: Classification, Clustering, and Applications , 2009 .

[69]  Eric P. Xing,et al.  MedLDA: maximum margin supervised topic models for regression and classification , 2009, ICML '09.

[70]  Yan Liu,et al.  Topic-link LDA: joint models of topic and author community , 2009, ICML '09.

[71]  James Allan,et al.  A Comparative Study of Utilizing Topic Models for Information Retrieval , 2009, ECIR.

[72]  David M. Blei,et al.  Relational Topic Models for Document Networks , 2009, AISTATS.

[73]  Berlin Chen,et al.  Word Topic Models for Spoken Document Retrieval and Transcription , 2009, TALIP.

[74]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[75]  Michael I. Jordan,et al.  DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification , 2008, NIPS.

[76]  James Allan,et al.  Evaluating topic models for information retrieval , 2008, CIKM '08.

[77]  Max Welling,et al.  Fast collapsed gibbs sampling for latent dirichlet allocation , 2008, KDD.

[78]  David M. Blei,et al.  Supervised Topic Models , 2007, NIPS.

[79]  Qiang Wu,et al.  McRank: Learning to Rank Using Multiple Classification and Gradient Boosting , 2007, NIPS.

[80]  A. McCallum,et al.  Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[81]  Sheng Tang,et al.  LDA-Based Retrieval Framework for Semantic News Video Retrieval , 2007, International Conference on Semantic Computing (ICSC 2007).

[82]  Hyunjo Lee,et al.  Context-Aware Architecture for Intelligent Application Services in Ubiquitous Computing , 2007, International Conference on Semantic Computing (ICSC 2007).

[83]  Hang Li,et al.  AdaRank: a boosting algorithm for information retrieval , 2007, SIGIR.

[84]  Filip Radlinski,et al.  A support vector method for optimizing average precision , 2007, SIGIR.

[85]  Tie-Yan Liu,et al.  Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.

[86]  W. Bruce Croft,et al.  Linear feature-based models for information retrieval , 2007, Information Retrieval.

[87]  Mark Steyvers,et al.  Topics in semantic representation. , 2007, Psychological review.

[88]  Yong Chen,et al.  Using data mining techniques and rough set theory for language modeling , 2007, TALIP.

[89]  Thomas L. Griffiths,et al.  Probabilistic Topic Models , 2007 .

[90]  Danielle S. McNamara,et al.  Handbook of latent semantic analysis , 2007 .

[91]  Evangelos E. Milios,et al.  Latent Dirichlet Co-Clustering , 2006, Sixth International Conference on Data Mining (ICDM'06).

[92]  Quoc V. Le,et al.  Learning to Rank with Nonsmooth Cost Functions , 2006, NIPS.

[93]  William Stafford Noble,et al.  Support vector machine , 2013 .

[94]  Andrew McCallum,et al.  Topics over time: a non-Markov continuous-time model of topical trends , 2006, KDD '06.

[95]  W. Bruce Croft,et al.  LDA-based document models for ad-hoc retrieval , 2006, SIGIR.

[96]  Hanna M. Wallach,et al.  Topic modeling: beyond bag-of-words , 2006, ICML.

[97]  Wei Li,et al.  Pachinko allocation: DAG-structured mixture models of topic correlations , 2006, ICML.

[98]  Andrew McCallum,et al.  A Note on Topical N-grams , 2005 .

[99]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[100]  Ramesh Nallapati,et al.  Discriminative models for information retrieval , 2004, SIGIR '04.

[101]  CHENGXIANG ZHAI,et al.  A study of smoothing methods for language models applied to information retrieval , 2004, TOIS.

[102]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[103]  Andrei Broder,et al.  A taxonomy of web search , 2002, SIGF.

[104]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[105]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[106]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[107]  W. Michael Conklin,et al.  Monte Carlo Methods in Bayesian Computation , 2001, Technometrics.

[108]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[109]  J. Ibrahim,et al.  Monte Carlo Methods in Bayesian Computation , 2000 .

[110]  J. C. BurgesChristopher.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[111]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[112]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[113]  J. Pitman,et al.  The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator , 1997 .

[114]  U. M. Feyyad.  Data mining and knowledge discovery: making sense out of data , 1996 .

[115]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[116]  A. Zellner.  Optimal Information Processing and Bayes's Theorem , 1988 .

[117]  David Heath,et al.  De Finetti's Theorem on Exchangeable Variables , 1976 .

[118]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[119]  Sheng Wang,et al.  Supervised Topic Model with Consideration of User and Item , 2013, AAAI.

[120]  Quan Wang,et al.  Regularized Latent Semantic Indexing: A New Approach to Large-Scale Topic Modeling , 2013, TOIS.

[121]  Eric P. Xing,et al.  MedLDA: maximum margin supervised topic models , 2012, J. Mach. Learn. Res..

[122]  Hwanjo Yu,et al.  SVM Tutorial - Classification, Regression and Ranking , 2012, Handbook of Natural Computing.

[123]  Hanna Wallach,et al.  Structured Topic Models for Language , 2008 .

[124]  T. Minka.  Selection bias in the LETOR datasets , 2008 .

[125]  C. Elkan,et al.  Topic Models , 2008 .

[126]  James Allan,et al.  HARD Track Overview in TREC 2004 (Notebook) High Accuracy Retrieval from Documents , 2004 .

[127]  James Allan,et al.  HARD Track Overview in TREC 2003: High Accuracy Retrieval from Documents , 2003, TREC.

[128]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[129]  École d'été de probabilités de Saint-Flour,et al.  École d'été de probabilités de Saint-Flour XIII - 1983 , 1985 .

[130]  D. Aldous.  Exchangeability and related topics , 1985 .