ML for NLP - Archive

Meetings in 2011

Thurs 8 September (Loss Minimization)

• acl.ldc.upenn.edu/acl2003/main/pdfs/Och.pdf Och 2003, "Minimum Error Rate Training in Statistical Machine Translation"
• http://aclweb.org/anthology-new/D/D11/D11-1125.pdf Hopkins and May 2011, "Tuning as Ranking" (optional)

Thurs 15th September (Loss Minimization 2: MIRA)

• http://acl.ldc.upenn.edu/P/P05/P05-w11012.pdf McDonald , Crammer et al. 2005, "Online Large-margin Training of Dependency Parsers"
• http://www.seas.upenn.edu/~taskar/pubs/mmcfg.pdf Taskar, Klein et al. 2004, "Max-margin Parsing" (optional)

Thurs 22nd September (Infinite Bayesian Mixtures 1: Intro)

• http://drum.lib.umd.edu//handle/1903/10058 Resnik and Hardie 2010, "Gibbs Sampling for the Uninitiated" (sections 1.1, 2.2, 2.3, 2.4, skim the rest)

Thurs 22nd September (Infinite Bayesian Mixtures 2: Sampling)

• http://homepages.inf.ed.ac.uk/sgwater/papers/acl07-bhmm.pdf Goldwater and Griffiths 2007, "Fully Bayesian Unsupervised Part of Speech Tagging" (mostly figure 2)

Thurs 6th October (Bayesian Methods 3)

• http://palm.mindmodeling.org/cogsci2011/papers/0582/index.html Bonawitz, Denison et al. 2011, "A Simple Sequential Algorithm for Approximating Bayesian Inference"
• http://perso.telecom-paristech.fr/~cappe/papers/06particle-cmg.pdf Cappe, Godsill et al. 2006, "An Overview of Existing Methods and Recent

Thurs 6th October (Bayesian Methods 3)

• http://palm.mindmodeling.org/cogsci2011/papers/0582/index.html Bonawitz, Denison et al. 2011, "A Simple Sequential Algorithm for Approximating Bayesian Inference"
• http://perso.telecom-paristech.fr/~cappe/papers/06particle-cmg.pdf Cappe, Godsill et al. 2006, "An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo" (tutorial)

Thurs 13th October (Bayesian Methods 4)

• http://www.cs.berkeley.edu/~pliang/papers/hdppcfg-emnlp2007.pdf Liang, Petrov et al. 2007, "The Infinite PCFG using Hierarchical Dirichlet Processes"

Thurs 20th October (Bayesian Methods 4b)

• Continue with "The Infinite PCFG"

Thurs 27th October (Markov Logic: intro)

• http://www.cs.washington.edu/homes/pedrod/papers/pilp.pdf Domingos, Kok et al. 2008, "Markov Logic", sections 4-6 (1 and 2 if you need background)

Thurs 3rd November (Markov Logic: coreference)

Thurs 10th November (Reinforcement Learning: intro)

• http://www.google.co.uk/url?sa=t&rct=j&q=algorithms%20for%20reinforcement%20learning.%20&source=web&cd=1&ved=0CCQQFjAA&url=http%3A%2F%2Fwww.sztaki.hu%2F%7Eszcsaba%2Fpapers%2FRLAlgsInMDPs-lecture.pdf&ei=bv23TtzmDc_W8QOdhLXsBA&usg=AFQjCNF-uJL3fwEKAbSetcsoSpWcDCNT6Q&cad=rja Szepesvari, "Algorithms for Reinforcement Learning", chapters 1, 2, 4.1, 4.2 and 4.3

Thurs 10th November ((Reinforcement learning: application)

• ftp://ftp.cs.pitt.edu/web/projects/nlp/conf/interspeech2009/IS2009/PDF/AUTHOR/IS090703.PDF, Li, Williams et al. 2009, "Reinforcement Learning for Dialog Management using Least-Squares Policy Iteration and Fast Feature Selection"

Topics in 2011

• Syntactic Topic Models. Jordan L Boyd-Graber, David Blei. NIPS 08.
• Graphical Models over Multiple Strings Dreyer and Eisner, EMNLP 2009
• A Spectral Algorithm for Learning Hidden Markov Models. Hsu, Kakade, Zhang. COLT 2009. (anoop)
• Multi-Class Confidence Weighted Algorithms. Koby Crammer, Mark Dredze, Alex Kulesza. EMNLP 2009.
• Confidence-Weighted Linear Classification. ICML 2008. (anoop)
• The Dynamic Hierarchical Dirichlet Process. Lu Ren, David B. Dunson, and Lawrence Carin, ICML 2008
• Curriculum Learning. Yoshua Bengio, Jerome Louradour, Ronan Collobert and Jason Weston, ICML 2009
• Silva, R., Heller, K.A., and Ghahramani, Z. Analogical Reasoning with Relational Bayesian Sets (AISTATS-2007)
• Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data. Tran The Truyen, Dinh Phung, Hung Bui, Svetha Venkatesh. NIPS 08
• Learning from Measurements in Exponential Families. Percy Liang, Dan Klein, and Michael Jordan. ICML 09.
• Minimized Models for Unsupervised Part-of-Speech Tagging. Sujith Ravi and Kevin Knight, EMNLP 2009