[DL] First Call for Papers: 3rd Workshop Semantic Deep Learning (SemDeep-3) -- collocated with COLING 2018

Thierry Declerck declerck at dfki.de
Thu Jan 11 19:43:13 CET 2018



*Workshop on Semantic Deep Learning *collocated with COLING 



STRICT Paper Submission Deadline: May 25, 2018(11:50 pm CET)
Notification of Acceptance: June 20, 2018
Camera-Ready Papers Due: June 30, 2018
Workshop Dates: August 20-21, 2018
Conference Dates: August 20-25, 2018

With the experiences gained from two previous workshops on Semantic Deep 
we would like to take this endeavor one step further by providing a 
platform at COLING 2018
where researchers and professionals in computational linguistics are 
invited to report results and
systems on the possible contributions of Deep Learning to classic 
problems in semantic applications,
such as meaning representation, dependency parsing, semantic role 
labelling, word sense
disambiguation, semantic relation extraction, statistical relational 
learning, knowledge base
completion, or semantically grounded inference.

There are notable examples of contributions leveraging either deep 
neural architectures or distributed
representations learned via deep neural networks in the broad area of 
Semantic Web technologies.
These include, among others: (lightweight) ontology learning, ontology 
alignment , ontology annotation,
and ontology prediction. Ontologies, on the other hand, have been 
repeatedly utilized as background
knowledge for machine learning tasks. As an example, there is a myriad 
of hybrid approaches for
learning embeddings by jointly incorporating corpus-based evidence and 
semantic resources.
This interplay between structured knowledge and corpus-based approaches 
has given way to
knowledge-rich embeddings, which in turn have proven useful for tasks 
such as hypernym discovery ,
collocation discovery and classification, word sense disambiguation, and 
many others.

We thus invite submissions that illustrate how NLP can benefit from the 
interaction between deep learning
and Semantic Web resources and technologies. At the same time, we are 
interested in submissions that
show how knowledge representation can assist in deep learning tasks 
deployed in the field of NLP
and how knowledge representation systems can build on top of deep 
learning results, for example
in the field of Neural Machine Translation (NMT).

Structured knowledge in deep learning:
- neural networks and logic rules for semantic compositionality
- learning and applying knowledge graph embeddings to NLP tasks
- learning semantic similarity and encoding distances as knowledge graph
- ontology-based text classification
- multilingual resources for neural representations of linguistics
- semantic role labeling

Deep reasoning and inferences:
- commonsense reasoning and vector space models
- reasoning with deep learning methods

Learning knowledge representations with deep learning
-  deep learning methods for knowledge-base completion
- deep learning models for learning knowledge representations from text
- deep learning ontological annotations

Joint tasks:
- information retrieval and extraction with knowledge graphs and deep 
learning models
- knowledge-based deep word sense disambiguation and entity linking
- investigation of compatibilities and incompatibilities between deep 
learning and Semantic Web approaches

Authors are invited to submit papers describing original, unpublished
work, completed or in progress. The papers should be maximally 9
pages with maximally 2 additional pages for references.

The COLING 2018 templates must be used. Paper submission will be
electronic in PDF format through the SoftConf conference management system.
Workshop Proceedings will be published by COLING 2018.

Reviewing will be double-blind, so authors need to conceal their
identity. The paper should not include the authors' names and 
affiliations, nor any acknowledgements. Limit anonymized
self-references only to articles that are relevant for reviewers.

Luis Espinosa Anke, Cardiff University, UK
Thierry Declerck, German Research Centre for Artificial Intelligence 
(DFKI GmbH), Saarbrücken, Germany
Dagmar Gromann, Technical University Dresden (TU Dresden), Dresden, Germany

Kemo Adrian, Artificial Intelligence Research Institute (IIIA-CSIC), 
Bellaterra, Spain
Luu Ahn Tuan (Institute for Infocomm Research, Singapore)
Miguel Ballesteros, IBM T.J. Watson Research Center, Yorktown Heights, 
Jose Camacho-Collados, Sapienza University of Rome, Rome, Italy
Gerard Casamayor, Pompeu Fabra University, Spain
Stamatia Dasiopoulou, Pompeu Fabra University, Spain
Maarten Grachten, Austrian Research Institute for AI, Vienna, Austria
Dario Garcia-Casulla, Barcelona Supercomputing Center (BSC), Barcelona, 
Jorge Gracia Del R´ıo, Ontology Engineering Group, UPM, Spain
Jindrich Helcl, Charles University, Prague, Czech Republic
Dirk Hovy, Computer Science Department of the University of Copenhagen, 
Petya Osenova, Bulgarian Academy of Sciences, Sofia, Bulgaria
Martin Riedel, Hamburg University, Germany
Stephen Roller, Facebook AI Research
Francesco Ronzano, Pompeu Fabra University, Barcelona, Spain
Enrico Santus, The Hong Kong Polytechnic University, Hong Kong
Francois Scharffe, Axon Research, New York, USA
Vered Shwartz, Bar-Ilan University, Ramat Gan, Isreal
Kiril Simov, Bulgarian Academy of Sciences, Sofia, Bulgaria
Michael Spranger, Sony Computer Science Laboratories Inc., Tokyo, Japan
Armand Vilalta, Barcelona Supercomputing Center (BSC), Barcelona, Spain
Arkaitz Zubiaga, University of Warwick, Coventry, UK

Thierry Declerck,
Senior Consultant at DFKI GmbH, Language Technology Lab
Stuhlsatzenhausweg, 3
D-66123 Saarbruecken
Phone: +49 681 / 857 75-53 58
Fax: +49 681 / 857 75-53 38
email: declerck at dfki.de

Deutsches Forschungszentrum fuer Kuenstliche Intelligenz GmbH
Firmensitz: Trippstadter Strasse 122, D-67663 Kaiserslautern

Prof. Dr. Dr. h.c. mult. Wolfgang Wahlster (Vorsitzender)
Dr. Walter Olthoff

Vorsitzender des Aufsichtsrats:
Prof. Dr. h.c. Hans A. Aukes

Amtsgericht Kaiserslautern, HRB 2313

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