LINGUIST List 21.858
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Sat Feb 20 2010
Calls: Applied Ling, Computational Ling/Sweden
Editor for this issue: Kate Wu
<kate linguistlist.org>
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Directory
1. Swapna
Somasundaran,
TextGraphs-5: Graph-based Methods for Natural Language
Message 1: TextGraphs-5: Graph-based Methods for Natural Language
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Date: 19-Feb-2010
From: Swapna Somasundaran <swapnacs.pitt.edu>
Subject: TextGraphs-5: Graph-based Methods for Natural Language
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Full Title: TextGraphs-5: Graph-based Methods for Natural Language
Date: 16-Jul-2010 - 16-Jul-2010
Location: Uppsala, Sweden
Contact Person: Swapna Somasundaran
Meeting Email: textgraphs10gmail.com
Web Site: http://www.textgraphs.org/ws10/index.html
Linguistic Field(s): Applied Linguistics; Computational Linguistics
Call Deadline: 05-Apr-2010
Meeting Description:
TextGraphs-5: Graph-based Methods for Natural Language Processing
Workshop at ACL 2010
Association for Computational Linguistics Conference Uppsala, Sweden - July
16th, 2010 http://www.textgraphs.org/ws10/
Call for Papers
Deadline for paper submission: Monday, April 5th, 2010
TextGraphs is at its fifth edition! This shows that two seemingly distinct
disciplines, graph theoretic models to 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 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: Graph Methods for Opinion Analysis
For the fifth edition of TextGraphs, we propose the special theme: ìGraph
Methods for Opinion Analysisî. This choice is motivated by two important
factors: (1) advanced opinion analysis that aims to go beyond polarity
recognition necessitates the integration of syntactic, semantic and logic
structures and (2) previous work in NLP has shown that graph methods are very
well suited to represent and exploit such structures in learning systems.
The aim is to bring together researchers from graph theory and opinion analysis
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 ambitious
opinion analysis research and provide a new, challenging problem motivation for
research in graph algorithms.
Finally, as the field of opinion mining advances towards deeper analysis and
more complex systems, graphical approaches may become even more pertinent. For
instance, graphs may be employed to capture opinion dynamics over time, or to
model interactions between opinion expressions across multiple modalities and,
to realize this, new graph-based algorithms and inference methods may need to be
developed.
Suggested topics
We invite submissions on the following (but not limited to) general topics
(including those from the special theme):
- Graph methods for sentiment lexicon induction
- Analysis of blog and web linking structures
- Graph methods for sentiment/opinion propagation
- Graph representation of data for opinion analysis
- Synonym/antonym graphs and their usage to extrapolate semantic orientation
- Social graphs and opinion analysis
- 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
- Dynamic graph representations for NLP
- Comparative analysis of graph-based methods and traditional machine learning
Techniques for NLP applications
- Kernel Methods for Graphs, e.g. random walk, tree and sequence kernels
- 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: Monday, April 5th, 2010
- Notification of acceptance: Thursday, May 6th, 2010
- Submission of camera-ready articles: Friday, June 4th, 2010
- Workshop at ACL 2010: Friday, July 16th, 2010
Organizing Committee
- Carmen Banea, University of North Texas,
- Alessandro Moschitti, University of Trento,
- Swapna Somasundaran, University of Pittsburgh,
- Fabio Massimo Zanzotto, University of Rome ìTor Vergataî
Program Committee
- Giuseppe Carenini, University of British Columbia
- Monojit Choudhury, Microsoft Research, India
- William Cohen, Carnegie Mellon University
- Andras Csomai, Google Inc.
- Michael Gamon, Microsoft Research, Redmond
- Thomas Gartner, Fraunhofer Institute for Intelligent Analysis and Information
Systems
- Lise Getoor, University of Maryland
- Andrew Goldberg, University of Wisconsin, Madison
- Eduard Hovy, Information Sciences Institute
- Richard Johansson, Trento University
- Lillian Lee, Cornell University
- Smaranda Muresan, Rutgers University
- Fabrizio Sebastiani, Istituto di Scienza e Tecnologia dellíInformazione
- Veselin Stoyanov, Cornell University
- Carlo Strapparava, Istituto per la Ricerca Scientifica e Tecnologica
- Hiroya Takamura, Tokyo Institute of Technology
- Dragomir R. Radev, University of Michigan
- Lluis Marquez Villodre, Universidad Politecnica de Catalunya
- Theresa Wilson, University of Edinburgh
- Xiaojin Zhu, University of Wisconsin, Madison
- Michael Strube, EML research
- Ulf Brefeld, Yahoo!
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