Proceedings of the UCNLG+Eval: Language Generation and Evaluation Workshop

The Workshop on Language Generation and Evaluation (UCNLG+EVAL) took place in Edinburgh on 31st July 2011, as part of EMNLP'11. It was the fourth of the UCNLG workshops which have the general aims 1. to provide a forum for reporting and discussing corpus-oriented methods for generating language; 2. to foster cross-fertilisation between NLG and other fields where language is automatically generated; and 3. to promote the sharing of data and methods for the purpose of system building and comparative evaluation in all language generation research.

[1]  Lide Wu,et al.  A Fast Algorithm for Feature Selection in Conditional Maximum Entropy Modeling , 2003, EMNLP.

[2]  Jason Baldridge,et al.  Coupling CCG and Hybrid Logic Dependency Semantics , 2002, ACL.

[3]  Timothy Baldwin,et al.  Multiword Expressions: A Pain in the Neck for NLP , 2002, CICLing.

[4]  T. Florian Jaeger,et al.  Redundancy and reduction: Speakers manage syntactic information density , 2010, Cognitive Psychology.

[5]  Gitte Lindgaard,et al.  Improving accessibility to statistical graphs: the iGraph-Lite system , 2007, Assets '07.

[6]  Sérgio Curto.  Bootstrapping Multiple-Choice Tests , 2010 .

[7]  Dan Klein,et al.  Improved Inference for Unlexicalized Parsing , 2007, NAACL.

[8]  Robert Malouf,et al.  Wide Coverage Parsing with Stochastic Attribute Value Grammars , 2004 .

[9]  Eric P. Xing,et al.  Grafting-light: fast, incremental feature selection and structure learning of Markov random fields , 2010, KDD '10.

[10]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[11]  Brian Roark,et al.  Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm , 2004, ACL.

[12]  Davide Fossati,et al.  Aggregation Improves Learning: Experiments in Natural Language Generation for Intelligent Tutoring Systems , 2005, ACL.

[13]  Corinne Jörgensen,et al.  Attributes of Images in Describing Tasks , 1998, Inf. Process. Manag..

[14]  James Shaw,et al.  Ordering Among Premodifiers , 1999, ACL.

[15]  Miles Osborne,et al.  Estimation of Stochastic Attribute-Value Grammars using an Informative Sample , 2000, COLING.

[16]  R. Logie,et al.  When a graph is poorer than 100 words: A comparison of computerised natural language generation, human generated descriptions and graphical displays in neonatal intensive care , 2010 .

[17]  James R. Curran,et al.  Faster Parsing by Supertagger Adaptation , 2010, ACL.

[18]  Peng Wu,et al.  Recognizing the Intended Message of Line Graphs , 2010, Diagrams.

[19]  P. Donnan,et al.  Cost effectiveness of computer tailored and non-tailored smoking cessation letters in general practice: randomised controlled trial , 2001, BMJ : British Medical Journal.

[20]  Gregory Aist,et al.  Generating Questions Automatically from Informational Text , 2009 .

[21]  Catherine Plaisant,et al.  The challenge of information visualization evaluation , 2004, AVI.

[22]  Rena Torres Cacoullos,et al.  On the persistence of grammar in discourse formulas: a variationist study of that , 2009 .

[23]  Barbara Plank,et al.  Reversible Stochastic Attribute-Value Grammars , 2011, ACL.

[24]  Josef van Genabith,et al.  QuestionBank: Creating a Corpus of Parse-Annotated Questions , 2006, ACL.

[25]  Stefan Riezler,et al.  Incremental Feature Selection and l1 Regularization for Relaxed Maximum-Entropy Modeling , 2004, EMNLP.

[26]  Stephan Oepen,et al.  Maximum Entropy Models for Realization Ranking , 2005 .

[27]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[28]  J Eriksson.  Lessons from a failure : Generating tailored smoking cessation letters , 2003 .

[29]  Gertjan van Noord.  Self-Trained Bilexical Preferences to Improve Disambiguation Accuracy , 2007, Trends in Parsing Technology.

[30]  Alan F. Smeaton,et al.  Experiments on using semantic distances between words in image caption retrieval , 1996, SIGIR '96.

[31]  Mark Steedman,et al.  The syntactic process , 2004, Language, speech, and communication.

[32]  Gertjan van Noord,et al.  At Last Parsing Is Now Operational , 2006, JEPTALNRECITAL.

[33]  Mark Johnson,et al.  How the Statistical Revolution Changes (Computational) Linguistics , 2009 .

[34]  Daniël de Kok.  Discriminative features in reversible stochastic attribute-value grammars , 2011 .

[35]  Daniël de Kok.  Feature Selection for Fluency Ranking , 2010 .

[36]  Erik Velldal,et al.  Empirical Realization Ranking , 2009 .

[37]  Christian Rohrer,et al.  DESIGNING FEATURES FOR PARSE DISAMBIGUATION AND REALISATION RANKING , 2007 .

[38]  Martin M. Soubbotin.  Patterns of Potential Answer Expressions as Clues to the Right Answers , 2001, TREC.

[39]  Ann Bies,et al.  Bracketing Guidelines For Treebank II Style Penn Treebank Project , 1995 .

[40]  Jun'ichi Tsujii,et al.  Probabilistic Models for Disambiguation of an HPSG-Based Chart Generator , 2005, IWPT.

[41]  R. Michael Young,et al.  Using Grice's maxim of Quantity to select the content of plan descriptions , 1999, Artif. Intell..

[42]  Thomas Hofmann,et al.  Speakers optimize information density through syntactic reduction , 2007 .

[43]  Marcel Worring,et al.  Classification of user image descriptions , 2004, Int. J. Hum. Comput. Stud..

[44]  Michael Elhadad,et al.  FUF: the Universal Unifier User Manual Version 5.2 , 1991 .

[45]  Roger Levy,et al.  Tregex and Tsurgeon: tools for querying and manipulating tree data structures , 2006, LREC.

[46]  Kathleen F. McCoy,et al.  Sight for visually impaired users: summarizing information graphics textually , 2010 .

[47]  Albert Gatt,et al.  Automatic generation of textual summaries from neonatal intensive care data , 2009 .

[48]  John A. Hawkins,et al.  Why are zero-marked phrases close to their heads? , 2003 .

[49]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[50]  G.J.M. van Noord,et al.  A Sentence Generator for Dutch , 2010 .

[51]  James R. Curran,et al.  Wide-Coverage Efficient Statistical Parsing with CCG and Log-Linear Models , 2007, Computational Linguistics.

[52]  Albert Gatt,et al.  From data to text in the Neonatal Intensive Care Unit: Using NLG technology for decision support and information management , 2009, AI Commun..

[53]  Sali A. Tagliamonte,et al.  No momentary fancy! The zero ‘complementizer’ in English dialects , 2005, English Language and Linguistics.

[54]  Jason Eisner,et al.  Lexical Semantics , 2020, The Handbook of English Linguistics.

[55]  Deb K. Roy,et al.  Learning visually grounded words and syntax for a scene description task , 2002, Comput. Speech Lang..

[56]  Dan Roth,et al.  Learning Question Classifiers , 2002, COLING.

[57]  I. Dan Melamed,et al.  Precision and Recall of Machine Translation , 2003, NAACL.

[58]  Ehud Reiter,et al.  Should Corpora Texts Be Gold Standards for NLG? , 2002, INLG.

[59]  Paul Piwek,et al.  Generating Questions from OpenLearn study units , 2009 .

[60]  Gertjan van Noord,et al.  The Alpino Dependency Treebank , 2001, CLIN.

[61]  Eduard H. Hovy,et al.  Learning surface text patterns for a Question Answering System , 2002, ACL.

[62]  Michael White,et al.  Perceptron Reranking for CCG Realization , 2009, EMNLP.

[63]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[64]  Rob Malouf,et al.  The Order of Prenominal Adjectives in Natural Language Generation , 2000, ACL.

[65]  Aoife Cahill.  Correlating Human and Automatic Evaluation of a German Surface Realiser , 2009, ACL/IJCNLP.

[66]  Mark Johnson,et al.  Exploiting auxiliary distributions in stochastic unification-based grammars , 2000, ANLP.

[67]  Nancy Green,et al.  Towards generating textual summaries of graphs , 2001, HCI.

[68]  Albert Gatt,et al.  BT-Nurse: computer generation of natural language shift summaries from complex heterogeneous medical data , 2011, J. Am. Medical Informatics Assoc..

[69]  Michael White,et al.  Efficient Realization of Coordinate Structures in Combinatory Categorial Grammar , 2006 .

[70]  James Theiler,et al.  Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space , 2003, J. Mach. Learn. Res..