Inducing Space Dirichlet Process Mixture Large-Margin Entity RelationshipInference in Knowledge Bases
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[1] C. Antoniak. Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .
[2] Oren Etzioni,et al. TextRunner: Open Information Extraction on the Web , 2007, NAACL.
[3] Andrew Y. Ng,et al. Improving Word Representations via Global Context and Multiple Word Prototypes , 2012, ACL.
[4] Daniel Jurafsky,et al. Learning Syntactic Patterns for Automatic Hypernym Discovery , 2004, NIPS.
[5] Xiao-Li Meng,et al. The EM Algorithm—an Old Folk‐song Sung to a Fast New Tune , 1997 .
[6] Thomas Hofmann,et al. Hidden Markov Support Vector Machines , 2003, ICML.
[7] Gerhard Weikum,et al. WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .
[8] Praveen Paritosh,et al. Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.
[9] Charles M. Bishop,et al. Variational Message Passing , 2005, J. Mach. Learn. Res..
[10] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[11] Jason Weston,et al. Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing , 2012, AISTATS.
[12] Michael I. Jordan,et al. Variational inference for Dirichlet process mixtures , 2006 .
[13] George A. Miller,et al. WordNet: A Lexical Database for English , 1995, HLT.
[14] T. Ferguson. A Bayesian Analysis of Some Nonparametric Problems , 1973 .
[15] Radford M. Neal. Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .
[16] Nicholas G. Polson,et al. Data augmentation for support vector machines , 2011 .
[17] Hans-Peter Kriegel,et al. A Three-Way Model for Collective Learning on Multi-Relational Data , 2011, ICML.
[18] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[19] Yee Whye Teh,et al. Stick-breaking Construction for the Indian Buffet Process , 2007, AISTATS.
[20] D. Blackwell,et al. Ferguson Distributions Via Polya Urn Schemes , 1973 .
[21] Nicolas Le Roux,et al. A latent factor model for highly multi-relational data , 2012, NIPS.
[22] Thomas L. Griffiths,et al. Infinite latent feature models and the Indian buffet process , 2005, NIPS.
[23] Hagai Attias,et al. A Variational Bayesian Framework for Graphical Models , 1999 .
[24] Purushottam W. Laud,et al. Bayesian Nonparametric Inference for Random Distributions and Related Functions , 1999 .
[25] Ning Chen,et al. Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines , 2011, ICML.
[26] Jun Zhou,et al. Mixing Linear SVMs for Nonlinear Classification , 2010, IEEE Transactions on Neural Networks.
[27] Oren Etzioni,et al. Identifying Relations for Open Information Extraction , 2011, EMNLP.
[28] Michael I. Jordan,et al. Variational methods for the Dirichlet process , 2004, ICML.
[29] Gerhard Weikum,et al. The SphereSearch Engine for Unified Ranked Retrieval of Heterogeneous XML and Web Documents , 2005, VLDB.
[30] Michael I. Jordan,et al. Bayesian parameter estimation via variational methods , 2000, Stat. Comput..
[31] Fernando A. Quintana,et al. Nonparametric Bayesian data analysis , 2004 .
[32] Samy Bengio,et al. A Parallel Mixture of SVMs for Very Large Scale Problems , 2001, Neural Computation.
[33] Joshua B. Tenenbaum,et al. Modelling Relational Data using Bayesian Clustered Tensor Factorization , 2009, NIPS.
[34] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[35] Jason Weston,et al. Learning Structured Embeddings of Knowledge Bases , 2011, AAAI.
[36] Christopher D. Manning,et al. Philosophers are Mortal: Inferring the Truth of Unseen Facts , 2013, CoNLL.
[37] Claire Gardent,et al. Improving Machine Learning Approaches to Coreference Resolution , 2002, ACL.
[38] Danqi Chen,et al. Reasoning With Neural Tensor Networks for Knowledge Base Completion , 2013, NIPS.