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--- courses:rg:wishlist [2011/03/24 18:11]
popel vytvořeno
+++ courses:rg:wishlist [2012/10/15 14:41]
popel papers about Tree kernels
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 ====== Wishlist ======
-  * Mark Johnson: [[http://acl.ldc.upenn.edu/D/D07/D07-1031.pdf|Why Doesn't EM Find Good HMM POS-Taggers?]] (Ondřej)
+==== Parsing ====
-  * Eugene Charniak: [[http://acl.ldc.upenn.edu/A/A00/A00-2018.pdf|A maximum-entropy-inspired parser]] (Zdeněk)
+  * Keith Hall: [[http://aclweb.org/anthology-new/P/P07/P07-1050.pdf|k-best Spanning Tree Parsing]] ACL 2007
+  * Introduction to MALT Parser (one of the many papers by Joakim Nivre) + one advance technique, e.g. [[http://www.aclweb.org/anthology-new/W/W09/W09-3811.pdf|An Improved Oracle for Dependency Parsing with Online Reordering]]
+  * 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)
+==== 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
+  * 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]]
+==== 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
+  *  Taro Watanabe, Eiichiro Sumit: [[http://www.aclweb.org/anthology/P/P11/P11-1125.pdf|Machine Translation System Combination by Confusion Forest]] ACL 2011
+  * Nan Duan, Mu Li, Ming Zhou: [[http://www.aclweb.org/anthology-new/P/P11/P11-1126.pdf|Hypothesis Mixture Decoding for Statistical Machine Translation]] ACL 2011
   * Abhishek Arun, Chris Dyer, Barry Haddow, Phil Blunsom, Adam Lopez and Philipp Koehn: [[http://www.aclweb.org/anthology/W/W09/W09-1114.pdf |Monte Carlo Inference and Maximization for Phrase-based Translation. Conference on Computational Natural Language Learning, 2009. ]]
   * Phil Blunsom, Trevor Cohn, Chris Dyer and Miles Osborne: [[http://homepages.inf.ed.ac.uk/pblunsom/pubs/blunsom-acl09.pdf|A Gibbs Sampler for Phrasal Synchronous Grammar Induction. ACL-IJCNLP 2009]]
   * Trevor Cohn and Phil Blunsom: [[http://homepages.inf.ed.ac.uk/pblunsom/pubs/cohn-blunsom-emnlp09.pdf|A Bayesian Model of Syntax-Directed Tree to String Grammar Induction. EMNLP 2009.]]
+==== 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.
+    - 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.
+    - 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?]]
+  * 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 ====
+  * <del>Jakob Uszkoreit, Thorsten Brants: [[http://www.aclweb.org/anthology-new/P/P08/P08-1086.pdf|Distributed Word Clustering for Large Scale Class-Based Language Modeling in Machine Translation]] ACL 2008</del>
+  * Helmut Schmid, Florian Laws: [[http://www.aclweb.org/anthology-new/C/C08/C08-1098.pdf|Estimation of Conditional Probabilities With Decision Trees and an Application to Fine-Grained POS Tagging]] Coling 2008
+  * Mark Johnson: [[http://acl.ldc.upenn.edu/D/D07/D07-1031.pdf|Why Doesn't EM Find Good HMM POS-Taggers?]] (Ondřej Bojar)
-A source of inspiration: [[http://www.statmt.org/ued/?n=Public.WeeklyMeeting|Edinburgh Reading Group]], [[http://www.aclweb.org/anthology-new/|ACL archive]], [[http://scholar.google.com]]
+==== A source of inspiration ====
+   * [[https://wiki.cs.umd.edu/mlrg/index.php?title=Spring11|Machine Learning RG: Large Data Stuff]]
+   * [[http://www.cs.utah.edu/~suresh/mediawiki/index.php/MLRG/spring10|Machine Learning RG: Structured prediction]]
+   * [[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.aclweb.org/anthology-new/|ACL archive]],I recommend trying the [[http://aclasb.dfki.de/|ACL Searchbench]]
+   * [[http://scholar.google.com]]

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