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Evaluation

The Call for Tasks for EVALITA 2016 is out together with the important dates of the initiative.

This message is on behalf of the “Towards EVALITA 2016” area chairs within the CLiC-it 2015 conference namely Rachele Sprugnoli (DH group, FBK), Viviana Patti (University of Turin), and Franco Cutugno (University Federico II of Naples).

The full set of training data for the EVENTI Task @EVALITA2014 is now available and can be downloaded following the links on the META-SHARE Platform https://sites.google.com/site/eventievalita2014/data-tools.

Registration form for EVALITA 2014 tasks is available!

Please register if you intend to participate in one or more tasks. Upon registration, task organizers will be able to contact you directly with news, updates, data availability etc.

We are happy to announce that the first part of the Training Data for the EVENTI task is now available for download. For instructions go to https://sites.google.com/site/eventievalita2014/data-tools

In addition, also the first version of our task guidelines is online: https://sites.google.com/site/eventievalita2014/file-cabinet

The call for interest for EVALITA 2014 tasks is open.

Academic institutions and industrial organizations interested in participating in the EVENTI task can fill in the form at the following address: http://www.evalita.it/CFI2014.html

The website of the EVENTI (EValuation of Events aNd Temporal Information) task at EVALITA 2014 is now online: https://sites.google.com/site/eventievalita2014/

Documentation, data and tools related to the task will be made available on the website.

The following hashtags can be used to disseminate the initiative through Twitter: #evalita2014 #EVENTI #Time.

The EVENTI - EValuation of Events aNd Temporal Information task by Tommaso Caselli (Trento-RISE), Rachele Sprugnoli (DH-FBK), Manuela Speranza (HLT-FBK) and Monica Monachini (ILC-CNR) has been accepted at EVALITA 2014 evaluation campaign.

Stefano Menini is among the organizers of SemEval 2014 task on "Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment".