No abstract

No abstract

In this paper a set of visualization tools and related ideas are introduced for extracting and analyzing social network features present within persistent, multi-speaker, multi-topic, quasi-synchronous computer-mediated communication systems enacted over the internet, i.e., internet chatrooms. Preliminary models of these tools are applied to a real-world chatroom dataset. Results suggest the utility of such tools for enhancing the usability of digital text communications and the understanding of social structures and dynamics within the virtual world. Potentially promising visualization methods and areas of future research are discussed.

Over the past decade, huge volumes of valuable information have become available to organizations. However, the existence of a substantial part of the information in unstructured form makes the automated extraction of business intelligence and decision support information from it difficult. By identifying the entities and their roles within unstructured text in a process known as semantic named entity recognition, unstructured text can be made more readily available for traditional business processes. The authors present a novel NER approach that is independent of the text language and subject domain making it applicable within different organizations. It departs from the natural language and machine learning methods in that it leverages the wide availability of huge amounts of data as well as high-performance computing to provide a data-intensive solution. Also, it does not rely on external resources such as dictionaries and gazettes for the language or domain knowledge.

No abstract