RAGS and beyond
This event took place on Wednesday 27 October 2004 at 12:45
Dr Roger Evans Information Technology Research Institute, University of Brighton
The RAGS project ('Reference Architecture for Generation Systems'; Brighton/Edinburgh, EPSRC) aimed to build a concrete infrastructure for collaborative Natural Language Generation (NLG) research, founded on an apparent emerging architectural consensus among NLG system builders. However, a detailed survey of these existing systems revealed that the 'consensus' was much less secure than it appeared at first sight. In order to achieve the goals of the project, we started to develop a much more sophisticated view of system architectures, flexible enough to accommodate existing research, yet precise enough to make a useful contribution as a collaborative 'plug-and-play' framework for NLG. The resulting approach asks interesting and challenging questions about the nature of data manipulation and functional 'modulehood' in large, complex, computational systems.
In this talk, I will describe the progressive development of these ideas, from the starting point of the problem revealed by the RAGS survey, through the RAGS two-level data model and functional architecture for NLG systems, and its implementation in the OASYS system, to subsequent work with Chris Mellish on functional vs implementation architectures, and my current ideas for developing a more generic architectural substrate.
This event took place on Wednesday 27 October 2004 at 12:45
The RAGS project ('Reference Architecture for Generation Systems'; Brighton/Edinburgh, EPSRC) aimed to build a concrete infrastructure for collaborative Natural Language Generation (NLG) research, founded on an apparent emerging architectural consensus among NLG system builders. However, a detailed survey of these existing systems revealed that the 'consensus' was much less secure than it appeared at first sight. In order to achieve the goals of the project, we started to develop a much more sophisticated view of system architectures, flexible enough to accommodate existing research, yet precise enough to make a useful contribution as a collaborative 'plug-and-play' framework for NLG. The resulting approach asks interesting and challenging questions about the nature of data manipulation and functional 'modulehood' in large, complex, computational systems.
In this talk, I will describe the progressive development of these ideas, from the starting point of the problem revealed by the RAGS survey, through the RAGS two-level data model and functional architecture for NLG systems, and its implementation in the OASYS system, to subsequent work with Chris Mellish on functional vs implementation architectures, and my current ideas for developing a more generic architectural substrate.
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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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