Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Referring Expression Generation in Situated Interaction

While most current frameworks for reference handling are based on binary truth-theoretic knowledge representation, in this thesis I argue for a perspective on reference which emphasises the collaborative nature of reference. I present the Probabilistic Reference And GRounding mechanism (PRAGR) which uses flexible concept assignment based on vague property models and situational context in order to maximise the chance of communicative success. I demonstrate that PRAGR is capable of dealing with several property domains with different internal structures, such as graded adjectives, colour, shape, projective terms, and projective regions. Further, I show that PRAGR is fit to handle in an integrated fashion the most relevant challenges of Referring Expression Generation, in particular graded properties, spatial relations, and salience effects. In three empirical evaluation studies, I demonstrate the usefulness of PRAGR for situated referential communication

Bringing Nordic mathematics education into the future : Preceedings of Norma 20 : The ninth Nordic conference on mathematics education Oslo, 2021

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Bringing Nordic mathematics education into the future : Preceedings of Norma 20 : The ninth Nordic conference on mathematics education Oslo, 2021

This volume presents Nordic mathematics education research, which will be presented at the Ninth Nordic Conference on Mathematics Education, NORMA 20, in Oslo, Norway, in June 2021. The theme of NORMA 20 regards what it takes or means to bring Nordic mathematics education into the future, highlighting that mathematics education is continuous and represents stability just as much as change.publishedVersio

eAssessment in engineering mathematics: gaps in perceptions of students and academics

This research investigated the perceptions held by first-year undergraduate engineering students and academics regarding the assessment of mathematics in online environments. The study was motivated by hearing students’ voices, in a moment of serendipity, and realizing that academics do not always hear those voices when teaching in online environments. Currently, there is no literature providing an insight to engineering students’ perceptions of eAssessment in Irish institutes of technology. The research considered students’ perceptions of self-efficacy, expectancy, motivation, and barriers to learning in parallel with those held by academics towards their students. The aim was to develop an understanding of students’ perceptions of eAssessment to help address the concerns of academics involved with online assessment of engineering mathematics. The population of interest in this study comprised first and second year undergraduate engineering students and academics from an Irish institute of technology as the principal group, and first year students from its higher education equivalent in six European countries. A convergent mixed methods design, where surveys and interview data were integrated, interpreted, and analysed, was employed. The convergent mixed methods design permitted flexibility in the data gathering stages to accommodate cultural and language differences within the academic and student populations. The findings of the research are presented under three themes: preparation for eAssessment and barriers to eAssessment; expectations, values, reward, and effort; motivational emotions and self-regulation. The three findings provide valuable insights and adds new knowledge to an understanding of the processes in eAssessment for engineering mathematics. Without listening to and hearing students’ voices, it is not possible for academics to gain an understanding of their students’ perceptions, emotions, and motivations. I therefore argue that higher education institutions take cognizance of the need for a meta-dialogue between students and academics to aid an understanding of the processes of eAssessment

Data quality issues in electronic health records for large-scale databases

Data Quality (DQ) in Electronic Health Records (EHRs) is one of the core functions that play a decisive role to improve the healthcare service quality. The DQ issues in EHRs are a noticeable trend to improve the introduction of an adaptive framework for interoperability and standards in Large-Scale Databases (LSDB) management systems. Therefore, large data communications are challenging in the traditional approaches to satisfy the needs of the consumers, as data is often not capture directly into the Database Management Systems (DBMS) in a seasonably enough fashion to enable their subsequent uses. In addition, large data plays a vital role in containing plenty of treasures for all the fields in the DBMS. EHRs technology provides portfolio management systems that allow HealthCare Organisations (HCOs) to deliver a higher quality of care to their patients than that which is possible with paper-based records. EHRs are in high demand for HCOs to run their daily services as increasing numbers of huge datasets occur every day. Efficient EHR systems reduce the data redundancy as well as the system application failure and increase the possibility to draw all necessary reports. However, one of the main challenges in developing efficient EHR systems is the inherent difficulty to coherently manage data from diverse heterogeneous sources. It is practically challenging to integrate diverse data into a global schema, which satisfies the need of users. The efficient management of EHR systems using an existing DBMS present challenges because of incompatibility and sometimes inconsistency of data structures. As a result, no common methodological approach is currently in existence to effectively solve every data integration problem. The challenges of the DQ issue raised the need to find an efficient way to integrate large EHRs from diverse heterogeneous sources. To handle and align a large dataset efficiently, the hybrid algorithm method with the logical combination of Fuzzy-Ontology along with a large-scale EHRs analysis platform has shown the results in term of improved accuracy. This study investigated and addressed the raised DQ issues to interventions to overcome these barriers and challenges, including the provision of EHRs as they pertain to DQ and has combined features to search, extract, filter, clean and integrate data to ensure that users can coherently create new consistent data sets. The study researched the design of a hybrid method based on Fuzzy-Ontology with performed mathematical simulations based on the Markov Chain Probability Model. The similarity measurement based on dynamic Hungarian algorithm was followed by the Design Science Research (DSR) methodology, which will increase the quality of service over HCOs in adaptive frameworks

Proceedings of the Seventh Congress of the European Society for Research in Mathematics Education

International audienceThis volume contains the Proceedings of the Seventh Congress of the European Society for Research in Mathematics Education (ERME), which took place 9-13 February 2011, at Rzeszñw in Poland

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways