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.
Previous editions of the series can be found here.
As the NAACL-HLT 2021 conference hosts TextGraphs-15, registration at the host conference is required to attend our workshop: https://2021.naacl.org/registration/.
Please select the W15: TextGraphs-15: Graph-based Methods for Natural Language Processing workshop during the registration.
TextGraphs-15 will be held on June 11 at from 10am till 7pm PDT (UTC−7).
Join us on Underline: https://underline.io/events/122/sessions?eventSessionId=4305!
TextGraphs-15 proceedings are available on the ACL Anthology at https://www.aclweb.org/anthology/volumes/2021.textgraphs-1/.
Title (Tentative): Reasoning with Language and Knowledge Graphs
Abstract: Knowledge can be implicitly encoded in large language models pre-trained on unstructured text or explicitly represented in structured knowledge graphs, such as Freebase or ConceptNet, where entities are represented as nodes and relations between them as edges. Language models have a broad coverage of knowledge, but they do not perform well on structured reasoning tasks, KGs, on the other hand, are more suited for structured reasoning, but may lack coverage and be noisy. In this talk I will discuss recent advancements in combining the strengths of language models and knowledge graphs for common-sense question answering as well as complex logical reasoning over knowledge graphs.
Bio: Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. His research focuses on machine learning and data mining large social, information, and biological networks. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, marketing, and biomedicine. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. It has also been featured in popular press outlets such as the New York Times and the Wall Street Journal. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, PhD in machine learning from Carnegie Mellon University and postdoctoral training at Cornell University.
Title: ENT-Rank: Finding Relevant Entities through Text and Knowledge Graphs
Abstract: The development of knowledge graph construction methods and the availability of large general-purpose knowledge graphs (KGs) have led to several advances in information retrieval (IR). For example, entity linking and KGs provide additional useful information about text and search queries, giving rise to more accurate models of relevance. This KG-enabled retrieval approach sets a new standard for several IR benchmarks. However, the exploitation of relational information in IR proves difficult for various reasons, such as low extraction recall, sparsity of schemas, and biases in the extraction pipeline. We suggest an approach for using entity-centric information in textual context, called ENT-Rank. ENT-Rank integrates different relevance-indicators from text and neighboring entities with features of entities. The empirical evaluation within TREC CAR demonstrates that this approach significantly improves the identification of relevant entities (or concepts) in response to open domain queries. One application of ENT-Rank is to create topic-specific knowledge graphs. ENT-Rank also contributes towards our long-term vision of creating new Wikipedia-like articles for new topics through methods in retrieval, extraction, and summarization.
Bio: Laura Dietz is an Assistant Professor at the University of New Hampshire, where she leads the lab for text retrieval, extraction, machine learning and analytics (TREMA). She organizes a tutorial/workshop series on Utilizing Knowledge Graphs in Text-centric Retrieval (KG4IR) and coordinates the TREC Complex Answer Retrieval Track. She received an NSF CAREER Award for utilizing fine-grained knowledge annotations in text understanding and retrieval. Previously, she was a research scientist in the Data and Web Science group at Mannheim University, and a research scientist with Bruce Croft and Andrew McCallum at the Center for Intelligent Information Retrieval (CIIR) at UMass Amherst. She obtained her doctoral degree with a thesis on topic models for networked data from Max Planck Institute for Informatics, supervised by Tobias Scheffer and Gerhard Weikum.
We invite submissions of up to eight (8) pages maximum, plus bibliography for long papers and four (4) pages, plus bibliography, for short papers.
The NAACL 2021 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://2021.naacl.org/calls/style-and-formatting/
Submit papers by the end of the deadline day (timezone is UTC-12) via our Softconf Submission Site: https://www.softconf.com/naacl2021/textgraphs2021
We are organizing a shared task before the workshop!
Many-hop multi-hop inference is challenging because there are often multiple ways of assembling a good explanation for a given question. This 2021 instantiation of the shared task focuses on the theme of determining relevance versus completeness in large multi-hop explanations. To this end, this year we include a very large dataset of approximately 250,000 expert-annotated relevancy ratings for facts ranked highly by baseline language models from previous years (e.g. BERT, RoBERTa).
Submissions using a variety of methods (graph-based or otherwise) are encouraged. Submissions that evaluate how well existing models designed on 2-hop multihop question answering datasets (e.g. HotPotQA, QASC, etc) perform at many-fact multi-hop explanation regeneration are welcome.
More information about the task held in TextGraphs-15 can be found here:
https://competitions.codalab.org/competitions/29228 (Overview and Submission)
https://github.com/cognitiveailab/tg2021task (Instructions and Baseline)
We welcome papers on the workshop topics even if you do not participate in the shared task.
Workshop Papers Due Date: March 22, 2021
Notification of Acceptance: April 15, 2021
Camera-ready Papers Due: April 26, 2021
Workshop Date: June 11, 2021
TextGraphs-15 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
Željko Agić, Unity Technologies, Denmark
Ilseyar Alimova, Kazan Federal University, Russian Federation
Amir Bakarov, Behavox, Russian Federation
Sivaji Bandyopadhyay, Jadavpur University, NIT Silchar, India
Tomáš Brychcín, University of West Bohemia, Czech Republic
Ruben Cartuyvels, Catholic University of Leuven, Belgium
Flavio Massimiliano Cecchini, Università Cattolica del Sacro Cuore, Italy
Devansh Chandak, Indian Institute of Technology Bombay, India
Mihail Chernoskutov, IMM UB RAS, Russian Federation
Yew Ken Chia, Singapore University of Technology and Design, Singapore
Rajarshi Das, University of Massachusetts, Amherst, USA
Stefano Faralli, University of Rome Unitelma Sapienza, Italy
Deborah Ferreira, University of Manchester, UK
Michael Flor, Educational Testing Service, USA
Debanjan Ghosh, Educational Testing Service, USA
Goran Glavaš, University of Mannheim, Germany
Natalia Grabar, CNRS STL UMR8163, Université de Lille, France
Aayushee Gupta, International Institute of Information Technology, Bangalore, India
Binod Gyawali, Educational Testing Service, USA
Carlos Gómez-Rodríguez, Universidade da Coruña, Spain
Rima Hazra, Indian Institute of Technology, Kharagpur, India
Tomáš Hercig, University of West Bohemia, Czech Republic
Mohammad Javad Hosseini, University of Edinburgh, Informatics, UK
Dmitry Ilvovsky, National Research University Higher School of Economics, Russian Federation
Ming Jiang, University of Illinois at Urbana-Champaign, USA
Sammy Khalife, Ecole Polytechnique, France
Rohith Gowtham Kodali, NATIONAL INSTITUTE OF TECHNOLOGY, India
Varun Madhavan, Indian Institute of Technology Kharagpur, India
Valentin Malykh, Huawei Noah's Ark Lab / Kazan Federal University, Russian Federation
Gabor Melli, OpenGov, USA
Alireza Mohammadshahi, IDIAP, Switzerland
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
Damien Nouvel, INALCO ERTIM, France
Aditya Girish Pawate, Indian Institute of Technology, Kharagpur, India
Jan Wira Gotama Putra, Tokyo Institute of Technology, Japan
Zimeng Qiu, Amazon Alexa AI, USA
Leonardo F. R. Ribeiro, TU Darmstadt, Germany
Michael Richter, Leipzig University, Germany
Stephen Roller, Facebook AI Research, USA
Minoru Sasaki, Ibaraki University, Japan
Viktor Schlegel, University of Manchester, UK
Rebecca Sharp, University of Arizona, USA
Artem Shelmanov, Skolkovo Institute of Science and Technology, Russian Federation
Konstantinos Skianis, BLUAI, Greece
Mark Steedman, University of Edinburgh, UK
Saatviga Sudhahar, Healx, UK
Mokanarangan Thayaparan, University of Manchester, UK
Antoine Tixier, Ecole Polytechnique, Palaiseau, France, France
Nicolas Turenne, BNU HKBU United International College (UIC), China
Adrian Ulges, RheinMain University of Applied Sciences, Germany
Vaibhav Vaibhav, Apple, USA
Serena Villata, Université Côte d’Azur, CNRS, Inria, I3S, France
Anssi Yli-Jyrä, University of Helsinki, Finland
Xiang Zhao, National University of Defense Technology, China
Alexander Panchenko, Skoltech
Fragkiskos D. Malliaros, CentraleSupélec, University of Paris-Saclay
Varvara Logacheva, Skoltech
Abhik Jana, University of Hamburg
Dmitry Ustalov, Yandex
Peter Jansen, School of Information, University of Arizona
Mokanarangan Thayaparan, University of Manchester
Marco Valentino, University of Manchester
Peter Jansen, School of Information, University of Arizona
Dmitry Ustalov, Yandex
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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TextGraphs-15 will be held on June 11 at from 10am till 7pm PDT (UTC−7).
Workshop schedule is present in the table below.