EMNLP 2015 - Conference
on Empirical Methods in Natural Language Processing
Date: September 17 – 21, 2015
Location: Culturgest, Lisbon
Online registration is
open until September 11, 2015
Students may apply for attendance grants
until August 26th 2015 here.
20th edition of the Conference on Empirical Methods in Natural
Language Processing (EMNLP 2015) is organized by the Association for Computational Linguistics (ACL - SIGDAT), a special interest group on linguistic data and corpus-based approaches to NLP. It is one of the main forums in which the scientists in the language technologies area disseminate their cutting-edge research work.
The local organization team includes not only staff from Instituto Superior Técnico, but also representatives from its spin-off community (Priberam and Unbabel), and its research institutes (INESC-ID and IT). In fact, one of the two local Chairs is André Martins, who was the first dual degree student of the Portugal Carnegie Mellon program in Language Technologies.
event is part of a
series of conferences on Empirical Methods in Natural Language Processing,
established as one of the main forums in which the scientists in the language
technologies area disseminate their cutting-edge research work.
The program schedule includes
invited talks by some of the world's best known researchers in the area: Yoshua
Bengio, who will talk about neural language models, and the recent impressive
results on machine translation and on mapping an image to a sentence, and
Justin Grimmer, who will discuss how elected officials and constituents communicate,
and show how representation style affects American politics.
The first two days of the
conference will be devoted to 7 parallel workshops and 8 tutorials. The list of
tutorials also highlights the growing impact of this type of data driven
approaches on topics such as personality analytics, semantic similarity,
information extraction, knowledge acquisition from the web, social media text
analysis, learning semantics from text, multiword expressions, and affect and