Semantic frame activation and contextual aptness of metaphorical expressions

The study aims to explore the possibility of semantic frame activation and interaction in metaphorical expressions, and the conditions of contextual aptness of metaphorical expressions. To ensure the appropriate level of ecological validity, experimental stimuli have been selected from a corpus of newspaper articles, and included in the norming procedures. The theoretical framework includes the investigations into context and semantic frames, semantic priming, discourse processing, conceptual metaphor theory, and psycholinguistic approaches to metaphor. The first four experiments were designed to test the activation and interaction of the organizing frames of source and target inputs in metaphorical expressions from the conceptual keys of CONFLICT and MOTION. This included an online priming paradigm with a categorization task, and the main dependent variable of interest was response time (RT). Stimuli were presented in (1) congruent metaphorical (metaphorical sentences), (2) congruent literal (literal sentences), and (3) incongruent conditions (unrelated sentences). Targets were individual words from the relevant frames. The results showed higher degrees of activation of the organizing frames of target inputs for both metaphor groups. There were no significant differences between the two congruent conditions, while RTs in the incongruent condition were significantly longer. The data seem to offer support for the interaction view of metaphor processing. The final two experiments tested the level of contextual aptness of target metaphorical expressions from the same two conceptual keys, in (1) congruent metaphorical (metaphor clusters), (2) congruent literal (literal paragraphs), and (3) incongruent priming conditions (unrelated paragraphs). Targets were metaphorical sentences from the two conceptual keys. The obtained results did not reveal any differences between the two congruent conditions, while the recorded RTs were significantly shorter in the incongruent condition. Overall, the study provides empirical insight into the phenomena of (metaphorical) framing, frame activation and interaction, and contextualization, and their import in online meaning construction

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Students´ language in computer-assisted tutoring of mathematical proofs

Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand. This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Digital Twins in Industry

Digital Twins in Industry is a compilation of works by authors with specific emphasis on industrial applications. Much of the research on digital twins has been conducted by the academia in both theoretical considerations and laboratory-based prototypes. Industry, while taking the lead on larger scale implementations of Digital Twins (DT) using sophisticated software, is concentrating on dedicated solutions that are not within the reach of the average-sized industries. This book covers 11 chapters of various implementations of DT. It provides an insight for companies who are contemplating the adaption of the DT technology, as well as researchers and senior students in exploring the potential of DT and its associated technologies