In our evaluation, we achieve high macro F1 scores (0. 76 - 0. 80 for the identification of argumentative units; 0. 86 - 0. 93 for their classification) on all datasets.
First, the contributions were categorised according to whether they contain a diabetes-specific information need or not, which might either be a non diabetes-specific information need or no information need at all, resulting in an agreement of 0. 89 (Krippendorff’s α).
Alpha matting describes the problem of separating the objects in the foreground from the background of an image given only a rough sketch.
We present our results for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019 - Task 6).
This paper describes our participation in the SemEval-2018 Task 12 Argument Reasoning Comprehension Task which calls to develop systems that, given a reason and a claim, predict the correct warrant from two opposing options.
In this Paper a system for solving SemEval-2017 Task 5 is presented.
In this work, we present the results of a systematic study to investigate the (commercial) benefits of automatic text summarization systems in a real world scenario.