TextGraphs 2020

14th Workshop on Graph-Based

Natural Language Processing

Workshop at COLING 2020

Barcelona, Spain

September 14, 2020


The workshops in the TextGraphs series have published and promoted the synergy between the field of Graph Theory and Natural Language Processing. Besides traditional NLP applications like word sense disambiguation and semantic role labeling, and information extraction graph-based solutions nowadays also target new web-scale applications like information propagation in social networks, rumor proliferation, e-reputation, language dynamics learning, and future events prediction, to name a few.


TextGraphs-14 has been successfully accepted as a single-day event at COLING (https://coling2020.org) in Barcelona, Spain!


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 rejections 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/

  • Workshop papers due May 20, 2020
  • Notification of acceptance: Jun 24, 2020
  • Camera-ready papers due Jul 11, 2020
  • Workshop date: Sep 14, 2020


We are organizing a shared task before the workshop. Our shared task on Explanation Regeneration asks participants to develop methods to reconstruct gold explanations for elementary science questions, using a new corpus of gold explanations that provides supervision and instrumentation for this multi-hop inference task. Each explanation is represented as an “explanation graph”, a set of atomic facts (between 1 and 16 per explanation, drawn from a knowledge base of 5,000 facts) that, together, form a detailed explanation for the reasoning required to answer and explain the resoning behind a question. Linking these facts to achieve strong performance at rebuilding the gold explanation graphs will require methods to perform multi-hop inference. The explanations include both core scientific facts as well as detailed world knowledge, allowing this task to appeal to those interested in both multi-hop reasoning and common-sense inference.

More information about the task held in TextGraphs 2019 can be found here:


TextGraphs-14 invites submissions on (but not limited to) the following topics:

  • Graph-based and graph-supported machine learning and deep learning methods
      • Graph 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 reasoning and interpreting deep neural networks
      • 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, Information 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,
      • Applications of label propagation algorithms, 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
      • 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 Semantic Web
      • Representation learning methods for knowledge graphs (i.e., knowledge graph embedding)
      • 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


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.

Connect with us on 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


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

  • Dmitry Ustalov, Yandex, Russia
  • Swapna Somasundaran, Educational Testing Service, USA
  • Alexander Panchenko, Skolkovo Institute of Science and Technology (Skoltech), Russia
  • Ioana Hulpus, Data and Web Science Group, University of Mannheim, Germany
  • Peter Jansen, School of Information, University of Arizona, USA
  • Fragkiskos D. Malliaros, CentraleSupélec, University of Paris-Saclay, France