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NLPEBDDLPT 2018 : NLP in the Era of Big Data, Deep Learning, and Post Truth | |||||||||||||||
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Call For Papers | |||||||||||||||
CALL FOR EXTENDED ABSTRACTS NLP in the Era of Big Data, Deep Learning, and Post Truth Workshop at ESSLLI 2018 August 13-17, 2018 INTRODUCTION Recent years have seen fast advances of the field of Natural Language Processing (NLP) due to the simultaneous influence of two revolutionary forces: Big Data and Deep Learning. The aim of using large corpora has been prominent in NLP since an earlier statistical, corpus-based revolution of the 1990s. Indeed, in corpus-based NLP size does matter, and researchers have been exploring corpora as large as the entire Web; now this abundance of data has enabled the return of Neural Networks and the rise of Deep Learning. More recently, we have further seen the rise of Big Data with its 3Vs: Volume, Velocity, and Variety. Even more recently, with the spread of fake news, it has been suggested that a fourth V should be considered: Veracity. The workshop welcomes work presenting new developments in applying NLP for solving problems related to Big Data, Deep Learning, and Veracity. We also invite discussion about the impact of these revolutionary forces on the field of NLP as a whole. TOPICS The workshop invites extended abstracts related to but not limited to the following: - Big Data for NLP - deep learning for NLP - automatic fact checking, stance detection, bias detection - Web as a corpus - work at the intersection of the above areas - position papers discussing the impact of the above on NLP IMPORTANT DATES Abstract submission: April 2, 2018 Author notification: May 2, 2018 Camera-ready version: May 14, 2018 Deadlines are midnight Pacific Standard Time (UTC−8). SUBMISSIONS Extended abstracts should present original, unpublished research and/or implementation results. We invite extended abstracts of up to two pages, excluding references. All submissions will be electronic and in PDF format, sent via the EasyChair system. Information about the author(s) and other identifying information such as obvious self- references and financial or personal acknowledgments should be omitted in the submitted abstracts whenever feasible. Extended abstracts may contain a clearly marked appendix and data files to support its claims. While reviewers are urged to consult this extra material for better comprehension, it is at their discretion whether they do so. Such extra material should also be anonymized to the extent feasible. Organizers Preslav Nakov (Qatar Computing Research Institute, HBKU) Ahmed Ali (Qatar Computing Research Institute, HBKU) Irina Temnikova (Sofia University) Georgi Georgiev (Ontotext) Lluis Marquez (Amazon) Shafiq Joty (Nanyang Technological University) Ivan Koychev (Sofia University) |
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