The SENTIment POLarity Classification Task 2016 (SENTIPOLC), is a rerun of the shared task on sentiment classification at the message level on Italian tweets proposed for the first time in 2014 for the Evalita evaluation campaign. It includes three subtasks: subjectivity classification, polarity classification, and irony detection. In 2016 SENTIPOLC has been again the most participated EVALITA task with a total of 57 submitted runs from 13 different teams. We present the datasets – which includes an enriched annotation scheme for dealing with the impact on polarity of a figurative use of language – the evaluation methodology, and discuss results and participating systems.

In recent years, natural language processing techniques have been used more and more in IR. Among other syntactic and semantic parsing are effective methods for the design of complex applications like for example question answering and sentiment analysis. Unfortunately, extracting feature representations suitable for machine learning algorithms from linguistic structures is typically difficult. In this paper, we describe one of the most advanced piece of technology for automatic engineering of syntactic and semantic patterns. This method merges together convolution dependency tree kernels with lexical similarities. It can efficiently and effectively measure the similarity between dependency structures, whose lexical nodes are in part or completely different. Its use in powerful algorithm such as Support Vector Machines (SVMs) allows for fast design of accurate automatic systems. We report some experiments on question classification, which show an unprecedented result, e.g. 41% of error reduction of the former state-of-the-art, along with the analysis of the nice properties of the approach. © 2011 ACM

The communication channels between physiotherapists and patients are many and varied. Rehabilitation robots are able to deliver intensive treatments and improve the patient's quality of life. However, rehabilitation robots in the literature do not integrate physical manipulation with natural verbal communication yet. This article proposes an innovative integrated system for motor rehabilitation based on the combination of physical and cognitive components to emulate the natural interaction between physiotherapists and patients. The proposed approach was validated in a laboratory setting with 20 healthy subjects. The cognitive system's ability to interact linguistically as well as the participants' kinematic performance and the emotional impact generated by two different robotic systems were assessed. The former integrates advanced linguistic capabilities and the latter lacks any verbal communication. The results showed that the presence of linguistic interaction promotes the quality of interaction, leading to improvements both in the execution of movements and in emotional terms.