====== Wishlist ====== ==== Parsing ==== * Goldberg & Orwant: [[http://www.aclweb.org/anthology/S13-1035.pdf|A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books]], 2013 * Yuan Zhang et al.: [[https://people.csail.mit.edu/regina/my_papers/inf14.pdf|Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees]], 2014 * Kong & Smith: [[http://arxiv.org/pdf/1404.4314v1.pdf|An Empirical Comparison of Parsing Methods for Stanford Dependencies]], 2014 * Ballesteros & Nivre: [[http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=9182723|Malt Optimizer: Fast and effective parser optimization]], 2014 * 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) * Reut Tsarfaty, Joakim Nivre, Evelina Andersson: [[http://aclweb.org/anthology-new/E/E12/E12-1006.pdf|Cross-Framework Evaluation for Statistical Parsing]], EACL 2012 === Treebanking === * Marneffe, Manning: [[http://www.aclweb.org/anthology/W08-1301.pdf| The Stanford typed dependencies representation]] (Rudolf Rosa) * (accompanied by [[http://nlp.stanford.edu/downloads/dependencies_manual.pdf|Stanford typed dependencies manual]]) * McDonald and other Google people: [[http://www.aclweb.org/anthology/P13-2017.pdf|Universal dependency annotation for multilingual parsing]] (Rudolf Rosa) * related: Petrov et al: [[http://arxiv.org/pdf/1104.2086v1.pdf|A universal part-of-speech tagset]] * HamleDT papers (Interset, HamleDT, coordinations) ==== Machine Learning ==== * Something about [[http://searn.hal3.name/|SEARN]], [[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. * 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]] * Structured prediction: * Introduction to structured prediction: [[http://people.mmci.uni-saarland.de/~titov/teaching/seminar-struct-prediction/struct-pred-class-01.pdf|Ivan Titov]] or [[http://nlpers.blogspot.cz/2006/04/what-is-structured-prediction.html|Hal Daumé]] have nice materials ([[http://nlpers.blogspot.cz/2006/01/structured-prediction-1-whats-out.html|Hal has many more]]). * 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 ==== * 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. * 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?]] * 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]] * 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. ==== Language and Vision ==== * Yansong Feng, Mirella Lapata: [[http://aclweb.org/anthology-new/N/N10/N10-1125.pdf|Topic Models for Image Annotation and Text Illustration]] * Farhadi, Hejrati, Sadeghi, Young: [[http://www.cs.cmu.edu/~afarhadi/papers/sentence.pdf|Every Picture Tells a Story: Generating Sentences from Images]] * Kojima, Tamura: [[http://www.cs.ucf.edu/courses/cap6412/2001/kojima.pdf|Natural Language Description of Human Activities from Video Images Based on Concept Hierarchy of Actions]] * Rohrbach, Regneri et al.: [[http://www.d2.mpi-inf.mpg.de/sites/default/files/rohrbach12eccv.pdf|Script Data for Attribute-based Recognition of Composite Activities]] ==== Unsupervised Approach to Morphology and Parsing ==== (TODO add some papers here :-)) ==== Other ==== * 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 * 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) * Petrovic, Mathews: [[http://homepages.inf.ed.ac.uk/s0894589/petrovic13unsupervised.pdf|Unsupervised joke generation from big data]] (Rudolf Rosa) ==== 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]]