Abstract:
In multimedia-based e-Learning systems, there are strong needs for segmenting lecture videos into topic units in order to organize the videos for browsing and to provide search capability. Automatic segmentation is highly desired because of the high cost of manual segmentation. While a lot of research has been conducted on topic segmentation of transcribed spoken text, most attempts rely on domain-specific cues and formal presentation format, and require extensive training; none of these features exist in lecture videos with unscripted and spontaneous speech. In addition, lecture videos usually have few scene changes, which implies that the visual information that most video segmentation methods rely on is not available. Furthermore, even when there are scene changes, they do not match with the topic transitions. In this paper, we make use of the transcribed speech text extracted from the audio track of video to segment lecture videos into topics. We review related research and propose a new segmentation approach. Our approach utilizes features such as noun phrases and combines multiple content-based and discourse-based features. Our preliminary results show that the noun phrases are salient features and the combination of multiple features is promising to improve segmentation accuracy.
Abstract:
In this paper, we evaluate mass knowledge acquisition using modified ALICE chatterbots. In particular we investigate the potential of allowing subjects to modify chatterbot responses to see if distributed learning from a web environment can succeed. This experiment looks at dividing knowledge into general conversation and domain specific categories for which we have selected telecommunications. It was found that subject participation in knowledge acquisition can contribute a significant improvement to both the conversational and telecommunications knowledge bases. We further found that participants were more satisfied with domain-specific responses rather than general conversation. © 2006 Elsevier Ltd. All rights reserved.
Abstract:
Nanotechnology research has experienced rapid growth in recent years. Advances in information technology enable efficient investigation of publications, their contents, and relationships for large sets of nanotechnology-related documents in order to assess the status of the field. This paper presents the development of a new knowledge mapping system, called Nano Mapper ( http://nanomapper.eller.arizona.edu ), which integrates the analysis of nanotechnology patents and research grants into a Web-based platform. The Nano Mapper system currently contains nanotechnology-related patents for 1976-2006 from the United States Patent and Trademark Office (USPTO), European Patent Office (EPO), and Japan Patent Office (JPO), as well as grant documents from the U.S. National Science Foundation (NSF) for the same time period. The system provides complex search functionalities, and makes available a set of analysis and visualization tools (statistics, trend graphs, citation networks, and content maps) that can be applied to different levels of analytical units (countries, institutions, technical fields) and for different time intervals. The paper shows important nanotechnology patenting activities at USPTO for 2005-2006 identified through the Nano Mapper system. © 2008 Springer Science+Business Media B.V.
