Connotation

Abstract: Purpose Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions. Design/methodology/approach Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed. Findings The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability. Originality/value By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

Abstract: The educational position of children of first-generation Turkish parents is widely considered problematic both in the Netherlands and elsewhere in Europe. Together with children from the Maghreb, Turkish children occupy the lowest position on the educational ladder. This however masks the fact that in twenty years spectacular gains in education have been made. More than a quarter of Turkish youth now enter higher education. This article describes these gains and tries to explain how they came about. A crucial factor is the changing attitude towards education in the Turkish community. Education increasingly has a positive connotation. Equally important is the growing expertise and knowledge available within the community. In the close-knit Turkish community, educational resources find their way to more and more youngsters in the form of help and support by older siblings and community projects.

Abstract: Aim. To identify the characteristics of conceptual metaphors with positive and negative connotations, in particular when used in the United Kingdom political discourse to describe Brexit. Methodology. Our analysis methodology includes cognitive-discourse analysis, contextual analysis and comparative method. Results. According to the results of our study, conceptual metaphors are widely used by politicians to express their attitudes and assessments of important socio-political events of the country. Research implications. The theoretical significance is the definition of an approach to the comparative cognitive-discourse study of metaphors with positive and negative connotations as a language tool used to describe socially significant political events. The practical significance is the possibility of applying the results in the University and postgraduate practice, in scientific research on stylistics and in the theory of discourse.

Abstract: We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles. To this end, we propose a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that have previously been shown to be highly effective for text understanding. Our method identifies the connotation of objects beyond their denotation: where most approaches to image understanding focus on the denotation of objects, i.e., their literal meaning, our work addresses the identification of connotations, i.e., iconic meanings of objects, to understand the message of images. We view image understanding as the task of representing an image-caption pair on the basis of a wide-coverage vocabulary of concepts such as the one provided by Wikipedia, and cast gist detection as a concept-ranking problem with image-caption pairs as queries. Our proposed algorithm brings together aspects of entity linking and clustering, subgraph selection, semantic relatedness, and learning-to-rank in a novel way. In addition to this novel task and a complete evaluation of our approach, we introduce a novel dataset to foster further research on this problem. To enable a throughout investigation of the problem of gist understanding, we produce a gold standard of over 300 image-caption pairs and over 8000 gist annotations covering a wide variety of topics at different levels of abstraction. We use this dataset to experimentally benchmark the contribution of different kinds of signals from heterogeneous sources, namely image and text. The best result with a Mean Average Precision (MAP) of 0.69 indicate that by combining both dimensions we are able to better understand the meaning of our image-caption pairs than when using language or vision information alone. Our supervised approach relies on the availability of human-annotated gold standard datasets. Annotating images with, possibly complex, topic labels is arguably a very time-consuming task that must rely on expert human annotators. We accordingly investigate whether parts of this process could be automatized using automatic image annotation and caption generation techniques. Our results indicate the general feasibility of an end-to-end approach to gist detection when replacing one of the two dimensions with automatically generated input, i.e., using automatically generated image tags or generated captions. However, we also show experimentally that state-of-the-art image and text understanding is better at understanding literal meanings of image-caption pairs, with non-literal pairs being instead generally more difficult to detect, thus paving the way for future work on understanding the message of images beyond their literal content.