Call for Paper: 16th Workshop on Graph-based Natural Language Processing (TextGraphs)
Venue: COLING 2022
Location: Gyeongju, Republic of Korea
Date: October 16, 2022
Papers Due: July 11, 2022 (Monday)
Workshop Description
For the past sixteen years, the workshops in the TextGraphs series have published and promoted the synergy between the field of Graph
Theory (GT) and Natural Language Processing (NLP). The mix between the two started small, with graph-theoretical frameworks providing efficient and elegant solutions for NLP applications. Graph-based solutions initially focused on single-document part-of-speech
tagging, word sense disambiguation, and semantic role labeling. They became progressively larger to include ontology learning and information extraction from large text collections. Nowadays, graph-based solutions also target Web-scale applications such as
information propagation in social networks, rumor proliferation, e-reputation, multiple entity detection, language dynamics learning, and future events prediction, to name a few.
We plan to encourage the description of novel NLP problems or applications that have emerged in recent years, which can be enhanced
with existing and new graph-based methods. The sixteenth edition of the TextGraphs workshop aims to extend the focus on graph-based representations for (1) integration and joint training and use of transformer-based models for graphs and text (such as Graph-BERT
and BERT), and (2) domain-specific natural language inference. Related to the former point, we would like to advance the state-of-the-art natural language understanding facilitated with large-scale language models like GPT-3 and linguistic relationships represented
by graph neural networks. Related to the latter point, we are interested in addressing a challenging task contributing to mathematical proof discovery. Furthermore, we also encourage research on applications of graph-based methods in knowledge graphs to link
them to related NLP problems and applications.
TextGraphs-16 invites submissions on (but not limited to) the following topics
§ 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
§ Papers
Due: July 11, 2022 (Monday)
§ Notification
of Acceptance: August 22, 2022 (Monday)
§ Camera-ready
papers due: September 5, 2022 (Monday)
§ Conference
date: October 12-17, 2022
§ Shared
task: TBD
Submission
§ 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
COLING 2022 templates must be used; these are provided in LaTeX and also Microsoft Word format. Submissions will only be accepted in PDF format. Download the Word and LaTeX templates here: https://coling2022.org/Cpapers.
§ Submit
papers by the end of the deadline day (timezone is UTC-12) via our Softconf Submission Site.
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. Also you can join us on Facebook: https://www.facebook.com/groups/900711756665369.
Organizers
§ Dmitry
Ustalov, Yandex
§ Yanjun
Gao, University of Wisconsin-Madison
§ Abhik
Jana, University of Hamburg
§ Thien
Huu Nguyen, University of Oregon
§ Gerald
Penn, University of Toronto
§ Arti
Ramesh, Binghamton University
§ Alexander
Panchenko, Skolkovo Institute of Science and Technology (Skoltech)
§ Mokanarangan
Thayaparan, University of Manchester & Idiap Research Institute
§ Marco
Valentino, University of Manchester & Idiap Research Institute
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Yanjun Gao, Ph.D. Computer Science and Engineering
Postdoctoral Research Associate, ICU Data Science Lab
Department of Medicine
School of Medicine and Public Health
University of Wisconsin-Madison