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--- courses:rg:wishlist [2012/10/15 14:41]
popel papers about Tree kernels
+++ courses:rg:wishlist [2013/01/10 15:07]
rosa +unsup
@@ 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.
   * 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 ====
   * 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
 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]].
     * 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
+     * Malte Nuhn, Arne Mauser, Hermann Ney: [[http://www-i6.informatik.rwth-aachen.de/publications/download/777/NuhnMalteMauserArneNeyHermann--DecipheringForeignLanguagebyCombiningLanguageModelsContextVectors--2012.pdf|Deciphering Foreign Language by Combining Language Models and Context Vectors]], 2012.
@@ Line 32: / Line 38: @@
 ==== MT Evaluation ====
   * Martin Popel would appreciate two RG meetings devoted to significance tests & MT evaluation. The two presenters should together read the following 4 papers (and related ones) and select two for presenting (one on bootstrap, one on approximate randomization).
-    - Stefan Riezler and John T. Maxwell III: [[http://acl.ldc.upenn.edu/W/W05/W05-0908.pdf|On Some Pitfalls in Automatic Evaluation and Significance Testing for MT]] (page 67) ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation, 2005.
+    - <del>Stefan Riezler and John T. Maxwell III: [[http://acl.ldc.upenn.edu/W/W05/W05-0908.pdf|On Some Pitfalls in Automatic Evaluation and Significance Testing for MT]] (page 67) ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation, 2005.</del>
     - Nicolas Stroppa, Karolina Owczarzak, Andy Way: [[http://doras.dcu.ie/15227/1/stroppa_owczarzak_07.pdf|A Cluster-Based Representation for Multi-System MT Evaluation]], 2007.
-    - Philipp Koehn: [[http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Koehn.pdf|Statistical significance tests for machine translation evaluation]], EMNLP 2004.
+    - <del>Philipp Koehn: [[http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Koehn.pdf|Statistical significance tests for machine translation evaluation]], EMNLP 2004.</del>
     - 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?]]
+  * T. Berg-Kirkpatrick, D. Burkett, D. Klein: [[http://www.aclweb.org/anthology/D/D12/D12-1091.pdf|An Empirical Investigation of Statistical Significance in NLP]]
   * 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.
+==== Unsupervised Approach to Morphology and Parsing ====
+(TODO add some papers here :-))
 ==== Other ====

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