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courses:rg:wishlist [2012/10/14 22:07]
popel 4 new suggestions 		courses:rg:wishlist [2012/10/16 13:41]
popel semiCRF suggested by Matěj Korvas
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 ==== Machine Learning ==== ==== Machine Learning ====
   * Something about <del>[[http://searn.hal3.name/|SEARN]]</del>, [[http://www.cs.utah.edu/~hal/megam/|MegaM]], [[http://hunch.net/~vw/|Vowpal Wabbit]] and/or its applications. [[courses:rg:2012:searn-in-practice|SEARN]] could be presented once again, if someone goes through the source codes.   * Something about <del>[[http://searn.hal3.name/|SEARN]]</del>, [[http://www.cs.utah.edu/~hal/megam/|MegaM]], [[http://hunch.net/~vw/|Vowpal Wabbit]] and/or its applications. [[courses:rg:2012:searn-in-practice|SEARN]] could be presented once again, if someone goes through the source codes.
-  * Andrew McCallum, Dayne Freitag, Fernando Pereira: [[http://www.ai.mit.edu/courses/6.891-nlp/READINGS/maxent.pdf|Maximum Entropy Markov Models for Information Extraction and Segmentation]], Conference on Machine Learning 2000, [[http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/gidofalvi.pdf|slides]] 
-  * John Lafferty, Andrew McCallum, Fernando Pereira: [[http://www.cis.upenn.edu/~pereira/papers/crf.pdf|Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data]], 2001 
   * Yoav Goldberg, Michael Elhadad: [[http://aclweb.org/anthology/P/P08/P08-2060.pdf|splitSVM: Fast, Space-Efficient, non-Heuristic, Polynomial Kernel   * Yoav Goldberg, Michael Elhadad: [[http://aclweb.org/anthology/P/P08/P08-2060.pdf|splitSVM: Fast, Space-Efficient, non-Heuristic, Polynomial Kernel
 Computation for NLP Applications]] ACL 2008 Computation for NLP Applications]] ACL 2008
   * Ryan McDonald, Keith Hall, Gideon Mann: [[http://aclweb.org/anthology-new/N/N10/N10-1069.pdf|Distributed Training Strategies for the Structured Perceptron]]   * Ryan McDonald, Keith Hall, Gideon Mann: [[http://aclweb.org/anthology-new/N/N10/N10-1069.pdf|Distributed Training Strategies for the Structured Perceptron]]
 +  * Kernels and Tree kernels:
 +    * Something about kernel methods in general (for SVM, perceptron etc.)
 +    * M. Collins and N. Duffy: [[http://www.cs.cmu.edu/Groups/NIPS/NIPS2001/papers/psgz/AA58.ps.gz|Convolution kernels for natural language]], NIPS 2001. And a [[http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.6355|related paper]].
 +    * Aron Culotta, Jeffrey Sorensen: [[http://www.newdesign.aclweb.org/anthology-new/P/P04/P04-1054.pdf|Dependency Tree Kernels for Relation Extraction]]
 +  * Structured prediction:
 +     * Introduction to structured prediction, maybe structured perceptron, see the slides at the end of [[http://people.mmci.uni-saarland.de/~titov/teaching/seminar-struct-prediction/index.html|Ivan Titov's course web]]
 +     * Andrew McCallum, Dayne Freitag, Fernando Pereira: [[http://www.ai.mit.edu/courses/6.891-nlp/READINGS/maxent.pdf|Maximum Entropy Markov Models for Information Extraction and Segmentation]], Conference on Machine Learning 2000, [[http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/gidofalvi.pdf|slides]]
 +     * John Lafferty, Andrew McCallum, Fernando Pereira: [[http://www.cis.upenn.edu/~pereira/papers/crf.pdf|Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data]], 2001
 +     * Sunita Sarawagi, William Cohen: [[http://www.cs.cmu.edu/~wcohen/postscript/semiCRF.pdf|Semi-Markov conditional random fields for information extraction]], Advances in Neural Information Processing Systems, 2004
 +
  
  
 ==== Machine Translation ==== ==== Machine Translation ====
 +  * Something about word alignment, recap IBM 1-5 (GIZA++), using word classes, HMM alignments. What is state of the art?
   * Ann Clifton, Anoop Sarkar: [[http://www.aclweb.org/anthology/P/P11/P11-1004.pdf|Combining Morpheme-based Machine Translation with Post-processing Morpheme Prediction]] ACL 2011   * Ann Clifton, Anoop Sarkar: [[http://www.aclweb.org/anthology/P/P11/P11-1004.pdf|Combining Morpheme-based Machine Translation with Post-processing Morpheme Prediction]] ACL 2011
   *  Taro Watanabe, Eiichiro Sumit: [[http://www.aclweb.org/anthology/P/P11/P11-1125.pdf|Machine Translation System Combination by Confusion Forest]] ACL 2011   *  Taro Watanabe, Eiichiro Sumit: [[http://www.aclweb.org/anthology/P/P11/P11-1125.pdf|Machine Translation System Combination by Confusion Forest]] ACL 2011
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     - Philipp Koehn: [[http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Koehn.pdf|Statistical significance tests for machine translation evaluation]], EMNLP 2004.     - Philipp Koehn: [[http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Koehn.pdf|Statistical significance tests for machine translation evaluation]], EMNLP 2004.
     - Ying Zhang, Stephan Vogel, Alex Waibel: [[http://www.lrec-conf.org/proceedings/lrec2004/pdf/755.pdf|Interpreting BLEU/NIST Scores: How Much Improvement Do We Need to Have a Better System?]]     - Ying Zhang, Stephan Vogel, Alex Waibel: [[http://www.lrec-conf.org/proceedings/lrec2004/pdf/755.pdf|Interpreting BLEU/NIST Scores: How Much Improvement Do We Need to Have a Better System?]]
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   * Chi-kiu LO and Dekai WU: [[http://www.cs.ust.hk/~dekai/library/WU_Dekai/LoWu_Acl2011.pdf|MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility via semantic frames. ACL HLT 2011]] (or other MEANT or HMEANT paper, but this one seems to be THE main one)   * Chi-kiu LO and Dekai WU: [[http://www.cs.ust.hk/~dekai/library/WU_Dekai/LoWu_Acl2011.pdf|MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility via semantic frames. ACL HLT 2011]] (or other MEANT or HMEANT paper, but this one seems to be THE main one)
 +  * Joseph P. Simmons, Leif D. Nelson, Uri Simonsohn: [[http://people.psych.cornell.edu/~jec7/pcd%20pubs/simmonsetal11.pdf|False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant]], Psychological Science, 2011. Yes, it is a psychological paper, but it is very valuable for anyone doing/reading any evaluation with significance tests.
 +
  
 ==== Other ==== ==== Other ====
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    * [[http://www.cs.utah.edu/~suresh/mediawiki/index.php/MLRG|Machine Learning RG: Semisupervised and Active Learning]]    * [[http://www.cs.utah.edu/~suresh/mediawiki/index.php/MLRG|Machine Learning RG: Semisupervised and Active Learning]]
    * [[http://www.statmt.org/ued/?n=Public.WeeklyMeeting|Edinburgh Reading Group]],    * [[http://www.statmt.org/ued/?n=Public.WeeklyMeeting|Edinburgh Reading Group]],
-   * [[http://www.aclweb.org/anthology-new/|ACL archive]], [[http://scholar.google.com]]+   * [[http://www.aclweb.org/anthology-new/|ACL archive]],I recommend trying the [[http://aclasb.dfki.de/|ACL Searchbench]] 
 +   * [[http://scholar.google.com]]

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