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TextGraphs 2011 : TextGraphs-6: Graph-based Methods for Natural Language Processing


Conference Series : Graph-based Methods for Natural Language Processing
When Jun 23, 2011 - Jun 23, 2011
Where Portland, Oregon
Submission Deadline Apr 1, 2011
Notification Due Apr 25, 2011
Final Version Due May 6, 2011

Call For Papers

TextGraphs-6: Graph-based Methods for Natural Language Processing

Workshop at ACL-HLT 2011
Association for Computational Linguistics Conference
Portland, Oregon

Deadline for paper submission: April 1st, 2011

TextGraphs is at its SIXTH edition! This shows that two seemingly distinct disciplines, graph theoretic models and computational linguistics, are in fact intimately connected, with a large variety of Natural Language Processing (NLP) applications adopting efficient and elegant solutions from graph-theoretical framework.

The TextGraphs workshop series addresses a broad spectrum of research areas and brings together specialists working on graph-based models and algorithms for natural language processing and computational linguistics, as well as on the theoretical foundations of related graph-based methods.

This workshop series is aimed at fostering an exchange of ideas by facilitating a discussion about both the techniques and the theoretical justification of the empirical results among the NLP community members. Spawning a deeper understanding of the basic theoretical principles involved, such interaction is vital to the further progress of graph-based NLP applications.

Special Theme: ''Graphs in Structured Input/Output Learning''

Recent work in machine learning has provided interesting approaches to globally represent and process structures, e.g.:
- graphical models, which encode observations, labels and their dependencies as nodes and edges of graphs;
- kernel-based machines which can encode graphs with structural kernels in the learning algorithms; and
- SVM-struct and other max margin methods and the structured perceptron that allow for outputting entire structures like for example graphs.

To make such methods effective both from efficiency and accuracy viewpoint, when using graphs as input and output, the typical graph properties must be exploited at the best. Our aim is to bring together researcher experts in graph theory and experts in machine learning for structure predictions from structured input in order to enable cross-fertilization of ideas. The proposed theme will encourage publication of early results and initiate discussions of issues in this area. We hope that this will help to shape future directions for improving both efficiency and accuracy of algorithms dealing with structured input/output, where graphs are seen as the main tool to model natural language processing data.

Special Issue:

High quality papers of Textgraphs-6 that are related to the special theme will be invited to submit to the JNLE special issue on ''Statistical Learning of Natural Language Structured Input and Output''

After receiving indications from the workshop organizers the authors will be invited to submit their journal version (see link above for the related guidelines). The schedule would be as indicated below:

* Submission of revised articles: 28 August 2011 (journal version)
* Final decisions to authors: 23 October 2011
* Final versions due from authors: 27 November 2011

Invited Speaker

We are delighted to announce that our invited speaker for TextGraph-6 will be Hal Daumé III.
He will give a talk in the topics of the special theme.

Suggested topics

We invite submissions on the following (but not limited to) general topics (including those from the special theme):
* Graphical and Conditional Models, e.g. Conditional Random Field, LDA
* Algorithms for graph output, e.g. structured SVMs, Perceptron
* Kernel Methods for Graphs, e.g. random walk, tree and sequence kernels
* Relational Learning using Graphs
* Integer Linear Programming with graph-based constraints
* Automatic Analysis of linking structures, e.g. web documents, blogs
* Graph methods for syntactic/semantic parsing
* Learning social graphs
* Graph-based representations, acquisition and evaluation of lexicon and ontology
* Dynamic graph representations for NLP
* Properties of lexical, semantic, syntactic and phonological graphs
* Clustering-based algorithms
* Application of spectral graph theory in NLP
* Unsupervised and semi-supervised learning models based-on graphs
* Graph methods for NLP tasks, e.g. morpho-syntactic annotation, word sense disambiguation, syntactic/semantic parsing
* Graph methods for NLP applications, e.g. retrieval, extraction and summarization of information
* Semantic inference using graphs, e.g. question answering and text entailment recognition

Important Dates

* Deadline for paper submission: April 1st, 2011
* Notification of acceptance: April 25th, 2011
* Submission of camera-ready articles: May 6th, 2011
* Workshop at ACL 2011: June 23th, 2011

Submission Information

* Formatting instructions
Submissions will consist of:
- regular full papers of max. 8 pages (one additional page for the Reference section only is allowed, for a maximum of 9 pages)
- regular short papers of max. 4 pages
- position papers of max. 4 pages (describing new scenarios for the use of graphs for text processing, especially in the field of Graphs in Structured Input/Output Learning).

All submissions must be electronic in PDF and must be formatted using the ACL-HLT 2011 Style Files (see

* Multiple-submission policy
Papers that have been or will be submitted to other meetings or publications must indicate this at submission time. Authors submitting multiple papers to TextGraphs-6 may not submit papers that overlap significantly (50%) with each other in content or results. Authors of papers accepted for presentation at Textgraphs-6 must notify the organizers immediately as to whether the paper will be presented. All accepted papers must be presented at the conference in order to appear in the proceedings.

* Blind review policy
In order to facilitate blind reviewing, the authors should omit their names and affiliations from the paper. Furthermore, self-references that reveal the author's identity, e.g., ''We previously showed (Smith, 1991) ...'' must be avoided. Instead, use citations such as ''Smith previously showed (Smith, 1991) ...'' Papers that do not conform to these requirements will be rejected without review.

* Submission
Papers should be submitted to the START Conference Manager at

Organizing Committee

* Irina Matveeva, Dieselpoint Inc.
* Alessandro Moschitti, University of Trento,
* Lluís Màrquez, Technical University of Catalonia
* Fabio Massimo Zanzotto, University of Rome ''Tor Vergata''

Program Committee

Eneko Agirre, University of the Basque Country
Roberto Basili, University of Rome, Tor Vergata
Ulf Brefeld, Yahoo Barcelona
Razvan Bunescu, Ohio University
Nicola Cancedda, Xerox Research Centre Europe
Giuseppe Carenini, University of British Columbia
Yejin Choi, Stony Brook University
William Cohen, Carnegie Mellon University
Andras Csomai, Google USA
Mona Diab, Columbia University
Gael Dias, Universidade da Beira Interior
Michael Gamon, Microsoft Research, Redmond
Thomas Gaertner, University of Bonn and Fraunhofer IAIS
Andrew Goldberg, University of Wisconsin
Richard Johansson, Trento University
Lillian Lee, Cornell University
Chris Manning, Stanford University
Ryan McDonald, Google Research
Rada Mihalcea, University of North Texas
Animesh Mukherjee, CSL Lab, ISI Foundation, Torino, Italy
Bo Pang, Yahoo! Research
Patrick Pantel, USC Information Sciences Institute
Daniele Pighin, Universidad Politecnica de Catalunya
Uwe Quasthoff, University of Leipzig
Dragomir Radev, University of Michigan
Dan Roth, University of Illinois at Urbana Champaign
Aitor Soroa, University of the Basque Country
Veselin Stoyanov, Johns Hopkins University
Swapna Sundaran, Siemens Corporate Research
Theresa Wilson, University of Edinburgh
Min Zhang, Institute for Infocomm Research, A-STAR

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