ESSLLI-2018 Workshop on NLP in the Era of Big Data, Deep Learning, and Post Truth

Submission Information

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Extended abstracts should present original, unpublished research and/or implementation results.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 (e.g., "We have shown in [12]") and financial or personal acknowledgements should be omitted in the submitted abstracts whenever feasible.

Submissions should be anonymous. Information about the author(s) and other identifying information such as obvious self-references (e.g., "We have shown in [12]") and financial or personal acknowledgements 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.

We use an adapted version of the ACL 2018 LaTeX template.

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