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TextGraphs 2020 : 14th Workshop on Graph-Based Natural Language Processing (TextGraphs-14)

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Conference Series : Graph-based Methods for Natural Language Processing
 
Link: https://sites.google.com/view/textgraphs2020/
 
When Sep 14, 2020 - Sep 14, 2020
Where Barcelona, Spain
Submission Deadline TBD
Categories    natural language processing   graph-based methods   machine learning
 

Call For Papers

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

Venue: COLING 2020 (https://coling2020.org)
Location: Online
Date: December 13, 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.

# Important Dates

- Oct 2, 2020: Workshop Paper Due Date
- Oct 25, 2020: Notification of Acceptance
- Nov 1, 2020: Camera-ready Papers Due
- Dec 13, 2020: Workshop Date

# Keynote Speakers

- Danai Koutra (http://web.eecs.umich.edu/~dkoutra/), Assistant Professor, University of Michigan
- Sujith Ravi (http://www.sravi.org), Director at Amazon Alexa AI
- Yizhou Sun (http://web.cs.ucla.edu/~yzsun/index.html), Associate Professor, UCLA

# 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TextGraphs invites the submission of long and short papers on original and unpublished research covering all aspects of graph-based natural language processing.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
- Exploration of capabilities and limitations of graph-based methods being applied to neural networks
- Investigation of aspects of neural networks that are (not) susceptible to graph-based analysis

* Graph-based methods for Information Retrieval, Infomation Extraction and Text Mining:
- Graph-based methods for word sense disambiguation
- Graph-based representations for ontology learning,
- 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
- Reranking with graphs


* New graph-based methods for NLP applications:
- Random walk methods in graphs
- Semi-supervised graph-based methods
- Dynamic graph representations
- Graph kernels

* Graph-based methods for applications on social networks
- Rumor proliferation
- E-reputation
- Multiple identity detection
- Language dynamics studies
- Surveillance systems

* Graph-based methods for NLP and the Semantic Web:
- Representation learning methods for knowledge graphs (i.e., knowledge graph embedding)
- Using graph-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


# 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, Paris-Saclay University, CentraleSupelec, Inria, France

# Social Media

Join us on Facebook: https://www.facebook.com/groups/900711756665369/
Follow us on Twitter: https://twitter.com/textgraphs
Join us on LinkedIn: https://www.linkedin.com/groups/4882867

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