We present research aiming to build tools for the normalization of User-Generated Content (UGC). We argue that processing this type of text requires the revisiting of the initial steps of Natural Language Processing (NLP), since UGC (micro-blog, blog, and, generally, Web 2.0 user generated texts) presents a number of non-standard communicative and linguistic characteristics -often closer to oral and colloquial language than to edited text. We present a corpus of UGC text in Spanish from three different sources: Twitter, consumer reviews and blogs, and describe its main characteristics. We motivate the need for UGC text normalization by analyzing the problems found when processing this type of text through a conventional language processing pipeline, particularly in the tasks of lemmatization and morphosyntactic tagging.

The paper tackles a central question in the field of Intelligent Computer-Assisted Language Learning (ICALL): How can language learning tasks be conceptualized and made explicit in a way that supports the pedagogical goals of current Foreign Language Teaching and Learning and at the same time provides an explicit characterization of the Natural Language Processing (NLP) requirements for providing feedback to learners completing those tasks? We argue that the successful implementation of language learning tasks that can be automatically assessed demands a design process in which both pedagogical and computational requirements feed each other. Extending well-established work in Task-Based Instruction (TBI) and Language Testing, we propose a framework that helps us (a) elucidate the formal features of foreign language learning activities, (b) characterize the relation between the expected and the actually elicited learner language, and (c) assess how the variability in the learner responses impacts interpretation and computational techniques for the automatic analysis of learner language. To validate our approach we apply the framework to two writing tasks for learners of English as a foreign language. Our analysis highlights the relevance of spelling out the pedagogical and linguistic goals of language learning tasks in order to successfully characterize the language expected and actually elicited in learner responses, to evaluate the realization of the task, and to support the design of effective NLP strategies. Given the combination of design-and data-driven perspectives, the framework supports an iterative approach to the creation of language learning tasks and ICALL materials.

Abstract. Children with attention deficit/hyperactivity disorder (ADHD) show deficient reading skills, which, like ADHD symptoms, are associated with limitations in neurocognitive abilities. Neurofeedback (NF) aims to improve the latter, to alleviate ADHD symptoms, and to promote school and reading performances. Whether frontal lobe-NF based on functional near-infrared spectroscopy (fNIRS) and electromyogram (EMG)-biofeedback (BF), however, improve reading abilities of children with ADHD and whether these changes are associated with changes in neurocognitive abilities, has not yet been clarified. It was also unclear whether embedding trainings in virtual reality (VR) could increase their effectiveness. These questions were examined using data of 35 children with ADHD (6–11 years) who participated in 15 sessions of fNIRS-NF in VR, fNIRS-NF in 2D, or EMG-BF in VR. On average, children's reading performance improved in all training groups. Stronger improvements were found after VR trainings. Improvements in reading natural words were, on a trend level, accompanied by decreasing attention, while improvements in reading pseudowords were accompanied by improved sustained attention and response inhibition. The results suggest that fNIRS-NF and EMG-BF effectively improve reading abilities of children with ADHD, especially when training in VR.

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The purpose of this paper is to present a strategy for the provision of language courses to learners of Spanish for specific purposes with intelligent feedback. As a byproduct, students can be recommended to proceed through a slightly different learning path in order to overcome their shortcomings.The paper is structured in 5 sections: section 1 is an introduction to this research work; section 2 describes the didactic assumptions and the learning environment in which the strategy is applied; section 3 presents the tools for both linguistic analysis and error detection, and describes how the system generates feedback; and section 4 concludes with some remarks and presents future work.