Connotation

About: Connotation is a research topic. Over the lifetime, 2096 publications have been published within this topic receiving 8265 citations.

From Cultural Tolerance to Mutual Cultural Respect: An Asian Artist’s Perspective on Virtual World Cultural Appropriation

Abstract: Culture is to be lived and to be learned. The connotation of cultural symbols is negotiated and learned within a culture (Sturken & Cartwright, 2004). When we are part of the dominant culture using another’s cultural objects, we may not know the context of that object, which may lead to cultural appropriation. In this paper, I review appropriation through three different domains: appropriation in art, appropriation in media and technology, and appropriation in cultural studies. I specifically chose to use denotation and connotation as the means to analyze interview text and visual data because these two coding systems are able to draw the cultural meanings embedded in the texts beyond the surface level of understanding. Findings can be categorized into three threads: 1) cultural appropriation in virtual worlds, 2) caring about cultural appropriation, and 3) solutions to cultural appropriation in virtual worlds. This research suggests that cultural exchange and mutual respect are the solutions to cultural appropriation in virtual worlds. Visual literacy will help virtual world residents learn how to read, see, decode, and create virtual imagery.

Concepts and Contingencies in Heritage Politics

Abstract: The booming field of current heritage studies is complex, versatile, and often characterized by contradictory significance or interpretation, as claims for heritage can appear to be simultaneously uplifting and profoundly problematic. In essence, heritage is a value-laden concept that can never assume a neutral ground of connotation. Heritage indicates a mode of cultural production with reformative significance.

Knowledge-rich Image Gist Understanding Beyond Literal Meaning

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 has 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. To enable a thorough investigation of the problem of gist understanding, we produce a gold standard of over 300 image-caption pairs and over 8,000 gist annotations covering a wide variety of topics at different levels of abstraction. We use this dataset to experimentally benchmark the contribution 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. We test the robustness of our gist detection approach when receiving automatically generated input, i.e., using automatically generated image tags or generated captions, and prove the feasibility of an end-to-end automated process.