Venue: COLING 2022 Location: Gyeongju, Republic of Korea
Date: October 16, 2022 Papers Due: July 17, 2022 (Sunday) Website: https://sites.google.com/view/textgraphs2022 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 17, 2022 (Sunday) - Notification of Acceptance: August 28, 2022 (Sunday) - Camera-ready papers due: September 11, 2022 (Sunday) - Conference date: October 16, 2022 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: https://www.softconf.com/coling2022/TextGraphs-16/ Shared Task We invite participation in the 1st Shared Task on Natural Language Premise Selection associated with the 16th Workshop on Graph-Based Natural Language Processing (TextGraphs 2022). The task proposed this year is the Natural Language Premise Selection (NLPS) (Ferreira et al., 2020a), inspired by the field of automated theorem proving. The task of NLPS takes as input a mathematical statement, written in natural language, and outputs a set of relevant sentences (premises) that could support an end-user finding a proof for that mathematical statement. The premises are composed of supporting definitions and propositions that can act as explanations for the proof process: https://codalab.lisn.upsaclay.fr/competitions/5692 Contact Please direct all questions and inquiries to our official e-mail address ( [email protected]) 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 - Thein Huu Nguyen, University of Oregon - Gerald Penn, University of Toronto - Arti Ramesh, ETS AI Labs - Alexander Panchenko, Skolkovo Institute of Science and Technology - Mokanarangan Thayaparan, University of Manchester & Idiap Research Institute - Marco Valentino, University of Manchester & Idiap Research Institute
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