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Workshop on Text Mining and Generation (TMG) at KI-2022

  •   September 19 or 20, 2022
  •   One-day workshop
  •   Co-located with KI-2022
  •   Trier, Germany

Digital text data is available in large amounts and different granularities. Typical sources include social media posts, books, news articles, web pages or company reports, etc. A major challenge this text data imposes is that it is unstructured and must first be processed to make further analysis possible. At the same time, there are also many situations in which only structured data is available that is to be verbally explained—for instance, by Explainable AI. These contrasting scenarios lead to two complementary application areas: text mining and text generation. The aim of text mining is to analyze the content of unstructured text and extract (useful) structured information. In contrast, text generation attempts to (automatically) create text from structured information or knowledge that is for example stored in large language models. The goal of the TMG workshop is to bring these two perspectives together by eliciting research paper submissions that aim for bridging the gap between knowledge extraction and text generation. Since recent approaches to text mining and text generation are predominantly based on artificial intelligence (AI) methodologies, KI 2022 is a relevant venue to bring together AI researchers working on these two tasks.

Important Dates

All dates are calculated at 11:59 AoE
DateDescription
July 15, 2022Submission Due
August 20, 2022Author Notification
August 31, 2022Camera Ready
September 19 or 20, 2022Workshop Date (TBD)

Call for Papers

We welcome any submissions that deal with transforming the representation of data using techniques of natural language processing (NLP): (applied) research papers, theoretical papers, user studies or prospective papers. Topics include, but are not limited to, the following:

  • Answer generation for question answering.
  • Argument mining.
  • Ethical aspects of AI for text generation (e.g., mitigating bias, misinformation, etc.).
  • Generating descriptions for graph-based workflows.
  • Generating explanations in recommender systems.
  • Graph-to-text generation for knowledge graphs.
  • Methods for Explainable AI.
  • Information extraction.
  • Knowledge graph refinement, particularly featuring text-based signals.
  • Parsing argumentative structures in texts.
  • Pattern detection in log files.
  • Snippet generation for search results.
  • Summarization.
  • Workflow mining.

Submission Information

The submission of the papers should be in accordance to the GI-LNI style and have to be submitted via EasyChair (please select the track W6: Text Mining and Generation). Authors can submit three different types of papers:

  • Full Paper (up to 12 pages)
  • Short Paper (up to 6 pages)
  • Extended Abstract (up to 3 pages)
If selected for publication in the GI-LNI proceedings, authors will later get a possibility to submit an extended version of the paper disregarding their original submission format. The workshop is running a single-blind review process.

Keynote by Prof. Dr. Iryna Gurevych

Prof. Dr. Iryna Gurevych

Iryna Gurevych (PhD 2003, U. Duisburg-Essen, Germany) is professor of Computer Science and director of the Ubiquitous Knowledge Processing (UKP) Lab at the Technical University (TU) of Darmstadt in Germany. Her main research interests are in machine learning for large-scale language understanding and text semantics. Iryna's work has received numerous awards. Examples are the ACL fellow award 2020 and the first Hessian LOEWE Distinguished Chair award (2,5 mil. Euro) in 2021. Iryna is co-director of the NLP program within ELLIS, a European network of excellence in machine learning. She is currently the vice-president of the Association of Computational Linguistics.

Detect—Verify—Communicate: Combating Misinformation with More Realistic NLP

Dealing with misinformation is a grand challenge of the information society directed at equipping the computer users with effective tools for identifying and debunking misinformation. Current Natural Language Processing (NLP) including its fact-checking research fails to meet the expectations of real-life scenarios. In this talk, we show why the past work on fact-checking has not yet led to truly useful tools for managing misinformation, and discuss our ongoing work on more realistic solutions. NLP systems are expensive in terms of financial cost, computation, and manpower needed to create data for the learning process. With that in mind, we are pursuing research on detection of emerging misinformation topics to focus human attention on the most harmful, novel examples. Automatic methods for claim verification rely on large, high-quality datasets. To this end, we have constructed two corpora for fact checking, considering larger evidence documents and pushing the state of the art closer to the reality of combating misinformation. We further compare the capabilities of automatic, NLP-based approaches to what human fact checkers actually do, uncovering critical research directions for the future. To edify false beliefs, we are collaborating with cognitive scientists and psychologists to automatically detect and respond to attitudes of vaccine hesitancy, encouraging anti-vaxxers to change their minds with effective communication strategies.

Preliminary Schedule

StartEndEvent
09:0009:10Opening
09:1010:40Session 1
10:4011:00Break
11:0012:00Keynote
12:0013:00Break
13:0014:30Session 2
14:3014:50Break
14:5016:20Session 3
16:2016:30Break
16:3018:00Social event with poster session

Organizing and Program Committee

Mirko Lenz

Mirko Lenz

 info@mirko-lenz.de

 Trier University

Lorik Dumani

Lorik Dumani

 dumani@uni-trier.de

 Trier University

Philipp Heinrich

Philipp Heinrich

 philipp.heinrich@fau.de

 Friedrich-Alexander-Universität Erlangen-Nürnberg

Nathan Dykes

Nathan Dykes

 nathan.dykes@fau.de

 Friedrich-Alexander-Universität Erlangen-Nürnberg

Merlin Humml

Merlin Humml

 merlin.humml@fau.de

 Friedrich-Alexander-Universität Erlangen-Nürnberg

Alexander Bondarenko

Alexander Bondarenko

 alexander.bondarenko@informatik.uni-halle.de

 Martin Luther University Halle-Wittenberg

Shahbaz Syed

Shahbaz Syed

 shahbaz.syed@uni-leipzig.de

 Leipzig University

Prof. Dr. Adrian Ulges

Prof. Dr. Adrian Ulges

 adrian.ulges@hs-rm.de

 RheinMain University of Applied Sciences

Prof. Dr. Stephanie Evert

Prof. Dr. Stephanie Evert

 stephanie.evert@fau.de

 Friedrich-Alexander-Universität Erlangen-Nürnberg

Prof. Dr. Lutz Schröder

Prof. Dr. Lutz Schröder

 lutz.schroeder@fau.de

 Friedrich-Alexander-Universität Erlangen-Nürnberg

Prof. Dr. Achim Rettinger

Prof. Dr. Achim Rettinger

 rettinger@uni-trier.de

 Trier University

Prof. Dr. Martin Vogt

Prof. Dr. Martin Vogt

 vogt@hochschule-trier.de

 Trier University of Applied Sciences

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