TextGraphs-14: 14th Workshop on Graph-Based Natural Language Processing

Venue: COLING 2020 (https://coling2020.org)
Location: Barcelona, Spain
Date: September 14, 2020
Website: https://sites.google.com/view/textgraphs2020

# Workshop Description

TextGraphs, now going on for more than a decade, is a workshop series promoting the synergies between methods of the field of Graph Theory and Natural Language Processing.

The fourteenth edition of the TextGraphs workshop aims to extend the focus on issues and solutions for large-scale graphs, such as those derived for Web-scale knowledge acquisition or social networks, and graph-based and graph-supported machine learning and deep learning methods.

We plan to encourage the description of novel NLP problems or applications that have emerged in recent years, which can be addressed with existing and new graph-based methods. Furthermore, we also encourage research on applications of graph-based methods in the area of Semantic Web to link them to related NLP problems and applications.

# Workshop Topics

TextGraphs invites the submission of long and short papers on original and unpublished research covering all aspects of graph-based natural language processing. Relevant topics for the conference include, but are not limited to, the following (in alphabetical order):

Graph-based and graph-supported machine learning methods:
- Graph embeddings and their combinations with text embeddings
- Graph-based and graph-supported deep learning (e.g., graph-based recurrent and recursive networks)
- Probabilistic graphical models and structure learning methods

Graph-based methods for Information Retrieval and Extraction:
- Graph-based methods for word sense disambiguation
- Graph-based strategies for semantic relation identification
- Encoding semantic distances in graphs
- Graph-based techniques for text summarization simplification, and paraphrasing
- Graph-based techniques for document navigation and visualization

New graph-based methods for NLP applications:
- Random walk methods in graphs
- Semi-supervised graph-based methods
- Graph-based methods for applications on social networks

Graph-based methods for NLP and Semantic Web:
- Representation learning methods for knowledge graphs
- Using graphs-based methods to populate ontologies using textual data

# Important Dates

May 20, 2020: Workshop Paper Due Date
Jun 24, 2020: Notification of Acceptance
Jul 11, 2020: Camera-ready Papers Due
Sep 13, 2020: Workshop Date

# Submission

We invite submissions of up to nine (9) pages maximum, plus bibliography for long papers and four (4) pages, plus bibliography, for short papers.

The COLING’2020 templates must be used; these are provided in LaTeX and also Microsoft Word format. Submissions will only be accepted in PDF format. Deviations from the provided templates will result in rejection without review. Download the Word and LaTeX templates here: https://coling2020.org/coling2020.zip

Submit papers by the end of the deadline day (timezone is UTC-12) via our Softconf Submission Site: https://www.softconf.com/coling2020/TextGraphs/

# Program Committee

Željko Agić, Corti, Denmark
Prithviraj Ammanabrolu, Georgia Institute of Technology, USA
Martin Andrews, Red Dragon AI, Singapore
Tomáš Brychcín, University of West Bohemia, Czech Republic
Flavio Massimiliano Cecchini, Università Cattolica del Sacro Cuore, Italy
Tanmoy Chakraborty, Indraprastha Institute of Information Technology Delhi (IIIT-D), India
Chen Chen, Magagon Labs, USA
Jennifer D'Souza, TIB Leibniz Information Centre for Science and Technology, Germany
Stefano Faralli, University of Rome Unitelma Sapienza, Italy
Goran Glavaš, University of Mannheim, Germany
Carlos Gómez-Rodríguez, Universidade da Coruña, Spain
Binod Gyawali, Educational Testing Service, USA
Tomáš Hercig, University of West Bohemia, Czech Republic
Ming Jiang, University of Illinois at Urbana-Champaign, USA
Sammy Khalife, Ecole Polytechnique, France
Anne Lauscher, University of Mannheim, Germany
Gabor Melli, OpenGov, USA
Clayton Morrison, University of Arizona, USA
Animesh Mukherjee, IIT Kharagpur, India
Matthew Mulholland, Educational Testing Service, USA
Giannis Nikolentzos, Ecole Polytechnique, France
Enrique Noriega-Atala, The University of Arizona, USA
Jan Wira Gotama Putra, Tokyo Institute of Technology, Japan
Steffen Remus, Hamburg University, Germany
Brian Riordan, Educational Testing Service, USA
Natalie Schluter, IT University of Copenhagen, Denmark
Robert Schwarzenberg, German Research Center for Artificial Intelligence (DFKI), Germany
Rebecca Sharp, University of Arizona, USA
Konstantinos Skianis, Ecole Polytechnique, France
Saatviga Sudhahar, Healx, UK
Mihai Surdeanu, University of Arizona, USA
Yuki Tagawa, Fuji Xerox, Japan
Mokanarangan Thayaparan, University of Manchester, Sri Lanka
Antoine Tixier, Ecole Polytechnique, Palaiseau, France, France
Nicolas Turenne, BNU HKBU United International College (UIC), China
Serena Villata, Université Côte d’Azur, CNRS, Inria, I3S, France
Xiang Zhao, National University of Defense Technology, China

# Organizers

Dmitry Ustalov, Yandex, Russia
Swapna Somasundaran, Educational Testing Service, USA
Alexander Panchenko, Skoltech, Russia
Ioana Hulpuş, University of Mannheim, Germany
Peter Jansen, University of Arizona, USA
Fragkiskos D. Malliaros, University of Paris-Saclay, France

# Contact

Please direct all questions and inquiries to our official e-mail address (textgraphsOC@gmail.com) or contact any of the organizers via their individual emails.

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Follow us on Twitter: https://twitter.com/textgraphs
Join us on LinkedIn: https://www.linkedin.com/groups/4882867