A short review of Research based Teaching and Learning content in Natural Language Generation

As investment and demand for AI technologies in particular within industry continue to rise, so will the demand for training materials, tools and know-how for building NLG systems. This paper presents a qualitative scoping exercise, which analyses academic course content for the purpose of classifying and aligning NLG teaching and learning material with corresponding research concepts in the field. The goal is to assess the overall status of university training with respect to NLG and to what level is the course content research informed

Multidimensional opinion mining from social data

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This thesis focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm, and irony, from user-generated content represented across multiple social media platforms and in various media formats, like textual, visual, and audio. Mining people’s social opinions from social sources, such as social media platforms and newswires commenting sections, is a valuable business asset that can be utilised in many ways and in multiple domains, such as Politics, Finance, and Government. The main objective of this research is to investigate how a multidimensional approach to Social Opinion Mining affects fine-grained opinion search and summarisation at an aspect-based level and whether such a multidimensional approach outperforms single dimension approaches in the context of an extrinsic human evaluation conducted in a real-world context: the Malta Government Budget, where five social opinion dimensions are taken into consideration, namely subjectivity, sentiment polarity, emotion, irony, and sarcasm. This human evaluation determines whether the multidimensional opinion summarisation results provide added-value to potential end-users, such as policy-makers and decision-takers, thereby providing a nuanced voice to the general public on their social opinions on topics of a national importance. Results obtained indicate that a more fine-grained aspect-based opinion summary based on the combined dimensions of subjectivity, sentiment polarity, emotion, and sarcasm or irony is more informative and more useful than one based on sentiment polarity only. This research contributes towards the advancement of intelligent search and information retrieval from social data and impacts entities utilising Social Opinion Mining results towards effective policy formulation, policy-making, decision-making, and decision-taking at a strategic level

National Language Technology Platform (NLTP) : overall view

The work in progress on the CEF Action National Language Technology Platform (NLTP) is presented. The Action aims at combining the most advanced Language Technology (LT) tools and solutions in a new state-of-the-art, Artificial Intelligence (AI) driven, National Language Technology Platform (NLTP).peer-reviewe

National language technology platform for public administration

This article presents the work in progress on the collaborative project of several European countries to develop National Language Technology Platform (NLTP). The project aims at combining the most advanced Language Technology tools and solutions in a new, state-of-the-art, Artificial Intelligence driven, National Language Technology Platform for five EU/EEA official and lower-resourced languages.peer-reviewe

A Dataset of Multidimensional and Multilingual Social Opinions for Malta’s Annual Government Budget

This paper presents three high quality social opinion datasets in the socio-economic domain, specifically Malta's annual Government Budgets of 2018, 2019 and 2020. They contain over 6,000 online posts of user-generated content in English and/or Maltese, gathered from newswires and social networking services. These have been annotated for multiple opinion dimensions, namely subjectivity, sentiment polarity, emotion, sarcasm and irony, and in terms of negation, topic and language. These datasets are a valuable resource for developing Opinion Mining tools and Language Technologies, and can be used as a baseline for assessing the state-of-the-art and for developing new advanced analytical methods for Opinion Mining. Moreover, they can be used for policy formulation, policy-making, decision-making and decision-taking. This research can also support similar initiatives in other countries, studies in the socio-economic domain and applied in other areas, such as Politics, Finance, Marketing, Advertising, Sales and Education