Reading Group
From NLPWiki
Our focus is currently on Bayesian approaches to NLP. Here's a partial bibliography:
Contents |
Bayesian approaches to NLP
Bibliographies
Beal: [1]
NIPS 2005: [2]
Griffiths’s reading list: [3]
Seminal
T.S. Ferguson. A Bayesian analysis of some nonparametric problems. Annals of Statistics 1:209-230, 1973. http://www.jstor.org/view/00905364/di983860/98p00275/0]
C.E. Antoniak. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Annals of Statistics 2:1152-1174, 1974. [4]
Foundational
M.D. Escobar and M. West. Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association, 90:577-588, 1995. [5]
S.N. MacEachern and P. Muller. Estimating mixture of Dirichlet process models. Journal of Computational and Graphical Statistics, 7:223-238, 1998. [6]
R.M. Neal. Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9:249-265, 2000. [7]
C.E. Rasmussen. The Infinite Gaussian Mixture Model. NIPS, 2000. [8]
H. Ishwaran and L. James. Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association, 96:161-173, 2001. [9]
Graphical Models
D. McAllester, M. Collins, F. Pereira. Case-Factor Diagrams for Structured Probabilistic Modeling. ??
NLP, Clustering
D.M. Blei, T.L. Griffiths, M.I. Jordan, and J.B. Tenenbaum. Hierarchical topic models and the nested Chinese restaurant process. NIPS, 2004. [10]
Y.W. Teh, M.I. Jordan, M.J. Beal, and D.M. Blei. Hierarchical Dirichlet processes. NIPS, 2004. [11]
Y.W. Teh, M.I. Jordan, M.J. Beal, and D.M. Blei. Hierarchical Dirichlet Processes. Tech Report. Last updated: 8th Oct'04 [12]
T. Griffiths, M. Steyvers, D. Blei, and J. Tenenbaum. Integrating Topics and Syntax. In press, Advances in Neural Information Processing Systems (NIPS) 17, 2004. [13]
D. Blei, A. Ng, and M. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993-1022, January 2003. [14]
R. Madsen, D. Kauchak, C. Elkan. Modeling Word Burstiness Using the Dirichlet Distribution. ICML 2005
A. McCallum, A. Corrada-Emmanuel, X. Wang. Topic and Role Discovery in Social Networks. ??
X. Wang, N. Mohanty, A. McCallum. Group and Topic Discovery from Relations and Text. LinkKDD-2005.
A. McCallum, A. Corrada-Emmanuel, X. Wang. The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email.
NLP, Language Modeling
S. Goldwater, T. Griffiths, M. Johnson. Interpolating Between Types and Tokens by Estimating Power-Law Generators. NIPS 2005
D. MacKay, L. Bauman Peto. A Hierarchical Dirichlet Language Model. Natural Language Engineering 1(1). 1994.
Yeh Whye Teh. A Bayesian Interpretation of Kneser-Ney Smoothing (?). NIPS 2005 Workshop on Bayesian NLP. Draft available.
Software
Nonparametric Bayesian inference software, Yee Whye Teh: [15]
