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Tech Report kmi-00-13 Abstract


Using genre to support active participation in learning communities
Techreport ID: kmi-00-13
Date: 2000
Author(s): Trevor Collins, Paul Mulholland and Stuart Watt
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Many communities exist that learn and share information either partly or wholly online. These (wholly or partially) on-line communities share messages, documents, and other artefacts that contain useful community knowledge. Members of the community learn through this sharing process, and the growing archive they create forms a valuable learning resource for existing and new members of the community. Two main kinds of approach exist to support community members in accessing resources. The first kind associates each communal artefact with a conceptual structure that represents its meaning. This approach requires high levels of maintenance, especially when the community resource grows at a fast rate. The second uses statistical and text analysis techniques to (semi) automatically derive semantics from the resource. There is increasing evidence that artefacts constructed and shared within a community follow genres revealed in the structure of the artefacts and the terminology used. These implicit genres used in the community are invaluable to members in constructing and interpreting artefacts, but existing tools that support members in locating and classifying resources make little or no use of genre. Our preliminary findings demonstrate the potential of genre-sensitive classification and retrieval tools.
 
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Multimedia and Information Systems is...


Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.

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