International Scientific Event “3rd Workshop DL4LD” (hybrid event)
LDK 2023 – the 4th Conference on Language, Data, and Knowledge and the Cost Action CA18209 NexusLinguarum (https://nexuslinguarum.eu)
is glad to announce the 3rd Workshop DL4LD: Addressing Deep Learning, Relation Extraction, and Linguistic Data with a Case Study on The Bigger Analogy Test Set (BATS). The workshop will be held in a hybrid mode, so speakers and attendees can choose to participate onsite or online.
Supported by the NexusLinguarum COST Action CA18209.
In cooperation with The Institute of Croatian Language (http://ihjj.hr/)
CALL FOR PARTICIPATION
The workshop will be held in Vienna, Austria on September 13, 2023.
We welcome contributions from scholars, and researchers working in computational linguistics and data science, linguistics, computer science, etc. This workshop aims at bringing together relation extraction, deep learning, and neural approaches with linguistic linked data. We invite research papers, application descriptions, system demonstrations, and position papers that discuss the interconnection of both areas. The workshop is going to include a twofold session with the first part focusing on the general workshop presentations and the second part presentations on the multilingual linguistic data preparation for NLP experiments focusing on the case study of BATS. We suggest the researchers working on related languages join teams and present rich comparative case studies. Panel discussion includes “unorthodox” new ideas and overviews of the challenges and opportunities of multilingual data preparation for the BATS experiment.
The workshop presents an excellent opportunity for the exchange of ideas, insights, and the latest research focused on, but not limited to relation extraction, deep learning, and neural approaches with linguistic linked data in:
- Deep Learning for Linguistic Linked (Open) Data, modeling, resources & interlinking
- LLOD and Deep Learning for Digital Humanities
- Enhancement of language models with structured linguistic data
- Use cases combining language models and structured linguistic data
- Deep Learning and LLOD in NLP
- Deep learning and relation extraction
- Deep learning and knowledge graphs
- Multilingual data preparation for the BATS experiment
The keynote speaker:
Dr. Michael Cochez (https://www.cochez.nl/) is an Assistant Professor in the Learning and Representation group at the Vrije Universiteit Amsterdam and manager of the Discovery Lab (an ICAI lab in collaboration with Elsevier and the University of Amsterdam). He works on bridging the gap between machine learning and knowledge graphs. His research interests include embedding knowledge graphs for downstream machine learning tasks, dealing with missing information in graphs (link prediction, approximate graph query answering), and applications such as question answering and recommendations. Dr. Cochez obtained his BSc. degree from the University of Antwerp, Belgium, his MSc. and Ph.D. from the University of jyväskylä, Finland, and was a postdoc at Fraunhofer FIT, Germany.