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TextGraphs 2016 : Graph-based Methods for Natural Language Processing


When Jun 17, 2016 - Jun 17, 2016
Where San Diego, California, USA
Submission Deadline Feb 25, 2016
Notification Due Mar 20, 2016
Final Version Due Mar 30, 2016
Categories    NLP

Call For Papers


TextGraphs-10: Graph-based Methods for Natural Language Processing

Workshop at the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT) 2016

June 17, 2016
San Diego, California, USA
For the past ten years, the series of TextGraphs workshops have exposed and encouraged the synergy between the field of Graph Theory (GT) and Natural Language Processing (NLP). The mix between the two started small, with graph theoretical framework providing efficient and elegant solutions for NLP applications. Solutions focused on single documents for part-of-speech tagging, word sense disambiguation, and semantic role labelling, and got progressively larger with ontology learning and information extraction from large text collections. Nowadays, the solutions have reached web scale through new fields of research that focus on information propagation in social networks, rumor proliferation, e-reputation, multiple entity detection, language dynamics learning, and future events prediction to name a few.
The tenth edition of the TextGraphs workshop would be a new episode in the sequel, focused on issues and solutions for large-scale graphs, such as those derived for web-scale knowledge acquisition or social networks. We aim to encourage the description of novel NLP problems or applications that have emerged in recent years, which can be addressed with graph-based solutions, as well as novel graph-based methods. In this edition, we add a new focus area on the usage of graph-­based methods and NLP techniques to connect to resources and applications in the Semantic Web. Furthermore, we would also encourage research about graph-based methods in the field of Semantic Web to be connected to NLP related problems and applications. Bringing together researchers interested in Graph Theory applied to Natural Language Processing and Semantic Web will provide an environment for further integration of graph-based solutions into different research fields. This will lead to a deeper understanding of
new theories of graph-based algorithms, create new approaches, and widen the usage of graphs.
The target audience is comprised of researchers working on problems related to either Graph Theory or graph-based algorithms applied to Natural Language Processing, social media, and the Semantic Web.
TextGraphs-10 invites submissions on (but not limited to) the following topics:
* Graph-based methods for Information Retrieval, Information Extraction and Text Mining
* Graph-based methods for word sense disambiguation,
* Graph-based representations for ontology learning,
* Graph-based strategies for semantic relations identification,
* Encoding semantic distances in graphs,
* Graph-based techniques for document navigation and visualization,
* Reranking with graphs,
* Label Propagation, etc.
* New graph-based methods for NLP applications
* Random walk methods in graphs,
* Spectral graph clustering,
* Semi-supervised graph-based methods,
* Methods and analyses for statistical networks,
* Small world graphs,
* Dynamic graph representations,
* Topological and pretopological analysis of graphs, etc.
* Graph-based methods for applications on social networks
* Rumor proliferation,
* E-reputation,
* Multiple identity detection,
* Language dynamics studies,
* Surveillance systems, etc.
* Graph-based methods for NLP and Semantic Web
* Using graphs-based methods to populate ontologies using textual data,
* Inducing knowledge of ontologies into NLP applications using graphs,
* Merging ontologies with graph-based methods using NLP techniques.
All submission deadlines are at 11:59 p.m. PST
Submission deadline: February 25, 2016
Notification of acceptance: March 20, 2016
Submission of camera-ready copy: March 30, 2016
PROGRAM COMMITTEE (in alphabetic order)
Sivaji Bandyopadhyay, Jadavpur University, Kolkata, India
Pushpak Bhattacharyya, IIT Bombay, India
Chris Biemann, TU Darmstadt, Germany
Asif Ekbar, Indian Institute of Technology, Patna, India
Filip Ginter, University of Turku, Finland
Michael Glass, IBM T. J. Watson Research, USA
Nanda Kambhatla, IBM Research, India
Roman Klinger, University Stuttgart, Germany
Gabor Melli, VigLink, USA
Rada Mihalcea, University of Michigan, USA
Alessandro Moschitti, University Trento, Italy
Animesh Mukherjee, IIT Kharagpur, India
Philippe Muller, Paul Sabatier University, France
Preslav Nakov, Qatar Foundation, Qatar
Günter Neumann, DFKI, Saarbrücken, Germany
Arzucan Özgür, Bogazici University, Turkey
Sebastian Pado, Stuttgart University
Alexander Panchenko, TU Darmstadt, Germany
Simone Paolo Ponzetto, Universität Mannheim, Germany
Stephan Roller, Unversity of Texas at Austin, USA
Shourya Roy, Xerox Research, India
Anders Søgaard, University of Copenhagen, Denmark
Amarnag Subramanya, Google, USA
Partha Pratim Talukdar, Indian Institute of Science, Bangalore, India
Aline Villavicencio, Federal University of Rio Grande do Sul, Brazil
Piek Vossen, University Amsterdam, Netherlands
Fabio Massimo Zanzotto, University of Rome, Italy
V.G.Vinod Vydiswaran,
Tanmoy Chakraborty,
Martin Riedl,

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