Zornitsa Kozareva

TL;DR: This paper defines the task, describes the training and test data and the process of their creation, lists the participating systems (10 teams, 28 runs), and discusses their results.

TL;DR: SemEval-2013 Task 2: Sentiment Analysis in Twitter as discussed by the authors included two subtasks: A, an expression-level subtask, and B, a message-level subtask.

TL;DR: This article presented a brief overview of the main challenges in the extraction of semantic relations from English text, and discuss the shortcomings of previous data sets and shared tasks, and introduced a new task, which will be part of SemEval-2010: multi-way classification of mutually exclusive semantic relations between pairs of common nominals.

TL;DR: This work proposes a novel and unified deep learning architecture, and an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously.

TL;DR: The two systems that competed in this task as part of SemEval-2012 are described, and their results are compared to those achieved in previously published research.