Tools | Papers Reccomendations | Useful Links

Tools for Research

Unix Tools

The screen command. Cheatsheet, John's screen resources (John says: Note that the screen configuration files are named .screenrc and .screenrc.gen, and so you have to explicitly look for dotfiles to see them. The viewRepos.sh and launchRepositoryEditors.sh scripts are pretty specific to my directory arrangement and may need tweaking for others' setups.)

Text processing commands:

• Unix for Poets by Ken Church is a nice guide to using the unix tool set: tr,grep,sort,uniq,wc,rev,sed,awk,shuf,cat,tac,tail,cut,paste, etc. Basic stuff but super useful
• A quick reference: https://github.com/nschneid/unix-text-commands

LaTeX & PDFs & Bibliography Management

Bibliograph Management: JabRef,Mendeley Desktop, Zotero, Mendeley vs. Zotero

PDF Annotation: Foxit Reader, Okular

Online Editors: ShareLatex, WriteLatex, ShareLatex vs. WriteLatex

Mark's Beamer Tricks Presentation

LaTeX latexdiff and Siva's notes.

Packages: LaTeX package for Bayes Networks, TikZ package for Dependency graphs

Nathan's Notes

Research organization

Jason Eisner's advice

Ali's page

Large-Scale Computation

A (very) short introduction Hadoop

Eddie notes from Des' webpage

Machine learning & Coding libraries

ML Toolkits: WEKA, MALLET, NLTK

Libraries: Guava, Apache Commons and the FileUtils class

Neural Networks: Theano, Torch

General

Backup Projects using Git: Siva's Notes

Paper Recommendations

Please add papers you consider appropriate for ML-for-NLP. Please also add thematic categories that are not covered.

Statistical Significance Testing

• Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
  • http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.37.3325&rep=rep1&type=pdf

Loss Minimization

• MERT and variants:
  • http://www.cs.jhu.edu/~ozaidan/zmert/zaidan09_ZMERT.pdf Zaidan 2009, "Z-MERT: A Fully Configurable Open Source Tool for Minimum Error Rate Training of Machine Translation Systems"
  • http://www.statmt.org/book/ Koehn 2010 (book), "Statistical Machine Translation"
  • http://www.aclweb.org/anthology/P/P11/P11-2031.pdf Clark, Dyer et al. 2011, "Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability"
• MIRA:
  • http://dl.acm.org/citation.cfm?id=944936 Crammer and Singer 2003, "Ultraconservative Online Algorithms for Multiclass Problems"
  • http://dl.acm.org/citation.cfm?id=1613747 Chiang, Marton et al. 2008, "Online Large-Margin Training of Syntactic and Structural Translation Features"
• Parsing:
  • http://acl.ldc.upenn.edu/P/P96/P96-1024.pdf Goodman 1993, "Parsing Algorithms and Metrics"
  • http://acl.ldc.upenn.edu/N/N07/N07-1051.pdf Petrov and Klein 2007, "Improved Inference for Unlexicalized Parsing"
• Ranking
  • http://research.microsoft.com/pubs/68133/lambdarank.pdf C.J.C. Burges, R. Ragno, and Q.V. Le 2007 "Learning to Rank with Non-Smooth Cost Functions"

Bayesian Methods

• http://www.isi.edu/natural-language/people/bayes-with-tears.pdf Knight 2009, "Bayesian Inference with Tears"
• http://www.cs.berkeley.edu/~klein/papers/tutorial-acl2007.pdf Liang and Klein, 2007, "Structured Bayesian Nonparametric Mode with Variational Inference"
• http://cocosci.berkeley.edu/tom/papers/exemplar.pdf Shi, Feldman et al. "Performing Bayesian Inference With Exemplar Models"
• http://cocosci.berkeley.edu/tom/papers/mechanism.pdf Shi, Griffiths et al. "Exemplar Models as a Mechanism for Performing Bayesian Inference"

Topic Models

• http://www.cs.umass.edu/~wallach/publications/wallach09rethinking.pdf Wallach, Mimno et al. 2009, "Rethinking LDA: Why Priors Matter"

Sampling Methods

• http://www.cs.ubc.ca/%7Earnaud/doucet_defreitas_gordon_smcbookintro.ps Doucet, de Freitas et al. "An Introduction to Sequential Monte Carlo Methods"

Dynamical Systems

• http://www.cs.ubc.ca/~arnaud/doucet_johansen_tutorialPF.pdf Doucet and Johansen 2008, "A Tutorial on Particle Filtering and Smoothing: Fifteen Years Later"

Information Theory

• http://www-lmmb.ncifcrf.gov/~toms/paper/primer/primer.pdf
• http://cm.bell-labs.com/cm/ms/what/shannonday/shannon1948.pdf

Variational Methods

Probabilistic Generative Models

Graphical Models

Deep learning and Energy Based Models

Language Modeling

• http://ftp.cs.toronto.edu/pub/gh/MacKay+Peto-1995.pdf D. J. C. MacKay and L. Peto, “A hierarchical Dirichlet language model,” Natural language engineering , vol. 1, no. 3, pp. 1–19, 1995.

Rota

Empty.

Useful Links

• Machine Learning Q&A: http://metaoptimize.com
• Machine Learning blog: http://hunch.net/