Semi-automatic annotation and retrieval of visual content using the topic map technology

There are still major challenges in the area of automatic indexing and retrieval of multimedia content data for very large multimedia content corpora. Current indexing and retrieval applications still use keywords to index multimedia content and those keywords usually do not provide any knowledge about the semantic content of the data. With the increasing amount of multimedia content, it is inefficient to continue with this approach. In this paper, we describe the project DREAM, which addresses such challenges by proposing a new framework for semi-automatic annotation and retrieval of multimedia based on the semantic content. The framework uses the Topic Map Technology, as a tool to model the knowledge automatically extracted from the multimedia content using an Automatic Labelling Engine. We describe how we acquire knowledge from the content and represent this knowledge using the support of NLP to automatically generate Topic Maps. The framework is described in the context of film post-production.

Theory building with system dynamics: topic and research contributions

The article discusses various reports published within the issue, including the articles "Closing the Loop: Promoting Synergies with other Theory Building Approaches to Improve System Dynamics Practice," by Birgit Kopainsky and Luis Luna-Reyes, and "On improving dynamic decision-making: Implications from multiple-process cognitive theory," by Bent Bakken.

Doing well by talking good? A topic modelling-assisted discourse study of corporate social responsibility

Using the novel technique of topic modelling, this paper examines thematic patterns and their changes over time in a large corpus of corporate social responsibility (CSR) reports produced in the oil sector. Whereas previous research on corporate communications has been small-scale or interested in selected lexical aspects and thematic categories identified ex ante, our approach allows for thematic patterns to emerge from the data. The analysis reveals a number of major trends and topic shifts pointing to changing practices of CSR. Nowadays ‘people’, ‘communities’ and ‘rights’ seem to be given more prominence, whereas ‘environmental protection’ appears to be less relevant. Using more established corpus-based methods, we subsequently explore two top phrases - ‘human rights’ and ‘climate change’ that were identified as representative of the shifting thematic patterns. Our approach strikes a balance between the purely quantitative and qualitative methodologies and offers applied linguists new ways of exploring discourse in large collections of texts.