FOSTER D2.1 - Technical protocol for rich metadata categorization and content classification

FOSTER aims to set in place sustainable mechanisms for EU researchers to FOSTER OPEN SCIENCE in their daily workflow, supporting researchers optimizing their research visibility and impact and the adoption of EU open access policies in line with the EU objectives on Responsible Research & Innovation.<p></p> More specifically, the FOSTER objectives are to:<p></p> • Support different stakeholders, especially young researchers, in adopting open access in the context of the European Research Area (ERA) and in complying with the open access policies and rules of participation set out for Horizon 2020;<p></p> • Integrate open access principles and practice in the current research workflow by targeting the young researcher training environment;<p></p> • Strengthen the institutional training capacity to foster compliance with the open access policies of the ERA and Horizon 2020 (beyond the FOSTER project); <p></p> • Facilitate the adoption, reinforcement and implementation of open access policies from other European funders, in line with the EC’s recommendation, in partnership with PASTEUR4OA project.<p></p> As stated in the project Description of Work (DoW) these objectives will be pursued and achieved through the combination of 3 main activities: content identification, repacking and creation; creation of the FOSTER Portal; delivery of training.<p></p> The core activity of the Task T2.1 will be to define a basic quality control protocol for content, and map available content by target group, and content type in parallel with WP3 Task 3.1.<p></p> Training materials include the full range of classical (structured presentation slides) and multi-media content (short videos, interactive e-books, ) that clearly and succinctly frames a problem and offers a working solution, in support of the learning objectives of each target group, and the range of learning options to be used in WP4 (elearning, blended learning, self-learning).<p></p> The map of existing content metadata will be delivered to WP3 for best choice of system requirements for continuous and sustainable content aggregation, enhancement and content delivery via “Tasks 3.2 e-Learning Portal” and “Task 3.4 Content Upload”. The resulting content compilation will be tailored to each Target Group and delivered to WP4

Thumbs up? Sentiment Classification using Machine Learning Techniques

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.Comment: To appear in EMNLP-200

KACST Arabic Text Classification Project: Overview and Preliminary Results

Electronically formatted Arabic free-texts can be found in abundance these days on the World Wide Web, often linked to commercial enterprises and/or government organizations. Vast tracts of knowledge and relations lie hidden within these texts, knowledge that can be exploited once the correct intelligent tools have been identified and applied. For example, text mining may help with text classification and categorization. Text classification aims to automatically assign text to a predefined category based on identifiable linguistic features. Such a process has different useful applications including, but not restricted to, E-Mail spam detection, web pages content filtering, and automatic message routing. In this paper an overview of King Abdulaziz City for Science and Technology (KACST) Arabic Text Classification Project will be illustrated along with some preliminary results. This project will contribute to the better understanding and elaboration of Arabic text classification techniques

Boundaries of Semantic Distraction: Dominance and Lexicality Act at Retrieval

Three experiments investigated memory for semantic information with the goal of determining boundary conditions for the manifestation of semantic auditory distraction. Irrelevant speech disrupted the free recall of semantic category-exemplars to an equal degree regardless of whether the speech coincided with presentation or test phases of the task (Experiment 1) and occurred regardless of whether it comprised random words or coherent sentences (Experiment 2). The effects of background speech were greater when the irrelevant speech was semantically related to the to-be-remembered material, but only when the irrelevant words were high in output dominance (Experiment 3). The implications of these findings in relation to the processing of task material and the processing of background speech is discussed