2nd CALL FOR PAPERS FOR THE 5TH WORKSHOP ON SEMANTIC DEEP LEARNING SemDeep-5 at IJCAI 2019
Macau, China
== Update 1: Deadline Extended to May 12! ==
== Update 2: Proceedings will be published on the ACL Anthology! ==
Semantic Web (SW) technologies and Deep Learning (DL) share the goal of creating intelligent artifacts. Both disciplines have had a remarkable impact in data and knowledge analysis, as well as knowledge representation, and in fact constitute two complementary directions for modeling linguistic phenomena and solving semantically complex problems. In this context, and following the main foundations set in past editions, SemDeep-5 aims to bring together SW and DL research as well as industrial communities. SemDeep-5 is interested in contributions of DL to classic problems in semantic applications, such as: (semi-automated) ontology learning, ontology alignment, ontology annotation, duplicate recognition, ontology prediction, knowledge base completion, relation extraction, and semantically grounded inference, among many others. At the same time, we invite contributions that analyse the interaction of SW technologies and resources with DL architectures, such as knowledge-based embeddings, collocation discovery and classification, or lexical entailment, to name only a few. This workshop seeks to provide an invigorating environment where semantically challenging problems which appeal to both Semantic Web and Computational Linguistic communities are addressed and discussed.
We invite submissions on any approach combining Semantic Web technologies and Deep Learning and suggest the following topics.
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
Reasoning and inferences and deep learning
- commonsense reasoning and vector space models
- reasoning with deep learning methods
- reasoning and knowledge graphs to explain neural network predictions
Learning knowledge representations with deep learning
- deep learning methods for knowledge-base completion
- deep ontology learning
- deep learning models for learning knowledge representations from text
- deep learning ontological annotations
Website: http://www.dfki.de/~declerck/semdeep-5
IMPORTANT DATES
Submission deadline: May 12, 2019
Notification of acceptance: May 27, 2019
Camera-ready version: June 10, 2019
Workshop dates: Co-located with IJCAI (10-16 August 2019).
Exact date to be announced soon.
SUBMISSION INSTRUCTIONS
We invite three types of submissions:
- Long papers (max. 8 pages)
- Short papers presenting innovative not fully empirically validated ideas or position papers (max. 4 pages)
- Short system description papers for the challenge (max. 4 pages)
All papers need to follow the ACL formatting guidelines. There is no space limit for references. Word and Latex templates available here http://www.acl2019.org/EN/call-for-papers.xhtml.
==============================
WiC Challenge
The workshop includes a challenge (shared task) on evaluating contextualized word representations. The task is based on the WiC dataset (https://pilehvar.github.io/wic/, NAACL 2019) and targets different areas of lexical semantics including, but not limited to, sense representation, word sense disambiguation, and contextualised word embeddings. Training, development and test data will be provided for the participants.
More information about the challenge and how to participate can be found at https://competitions.codalab.org/competitions/20010. Important dates are shown below.
WiC Challenge dates:
Results posted: 16 April 2019
Submission of short system description paper: 3 May 2019
ORGANIZING COMMITTEE
Luis Espinosa Anke, Cardiff University, UK
Thierry Declerck, DFKI GmbH, Germany
Dagmar Gromann, Technical University Dresden, Germany
Jose Camacho-Collados, Cardiff University, UK
Mohammad Taher Pilehvar, Iran University of Science and Technology, Iran