[ Skip to the content ]

Institute of Formal and Applied Linguistics Wiki

[ Back to the navigation ]

You are here: start » courses » rg » wishlist

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision 		Previous revision
Next revision Both sides next revision
courses:rg:wishlist [2012/10/15 14:41]
popel papers about Tree kernels 		courses:rg:wishlist [2012/10/16 14:58]
popel
Line 7: Line 7:
   * Koo et al.: [[http://www.aclweb.org/anthology-new/D/D10/D10-1125.pdf|Dual Decomposition for Parsing with Non-Projective Head Automata]] EMNLP 2010.   * Koo et al.: [[http://www.aclweb.org/anthology-new/D/D10/D10-1125.pdf|Dual Decomposition for Parsing with Non-Projective Head Automata]] EMNLP 2010.
   * Eugene Charniak: [[http://www.aclweb.org/anthology-new/A/A00/A00-2018.pdf|A maximum-entropy-inspired parser]] (Zdeněk Žabokrtský)   * Eugene Charniak: [[http://www.aclweb.org/anthology-new/A/A00/A00-2018.pdf|A maximum-entropy-inspired parser]] (Zdeněk Žabokrtský)
 +  * Reut Tsarfaty, Joakim Nivre, Evelina Andersson: [[http://aclweb.org/anthology-new/E/E12/E12-1006.pdf|Cross-Framework Evaluation for Statistical Parsing]], EACL 2012
  
 ==== 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
Line 19: Line 18:
     * 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]].     * 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]]     * 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
 +
  
  

[ Back to the navigation ] [ Back to the content ]