Efficient data manipulation in document classification. Document Classification has an enormous relevance in an era where large amounts of textual information is available. Document Classification is based on statistical and machine learning techniques that model documents represented as points in a multidimensional space. The Computer Engineering Laboratory (CEL) has ongoing projects using neural networks and other techniques for document classification. We are developing a development environm ....Efficient data manipulation in document classification. Document Classification has an enormous relevance in an era where large amounts of textual information is available. Document Classification is based on statistical and machine learning techniques that model documents represented as points in a multidimensional space. The Computer Engineering Laboratory (CEL) has ongoing projects using neural networks and other techniques for document classification. We are developing a development environment for large classification tasks, and Prof. Lee¡¯s work will focus in managing large amounts of data for them. Using his experience in data compression, databases and web applications, he will produce a set of tools for handling Gigabytes of textual data in our classification environment.Read moreRead less
Effective Information Retrieval for Partitioned Document Collections. Current information retrieval services make use of massive indexes in order to resolve content-based queries. Monolithic approaches like this have been effective until now because the volume of data stored has been manageable on a single machine or tightly-coupled cluster of machines, and because the data has been available for collection. But with an increasing amount of automatically generated data, and an increasing diversi ....Effective Information Retrieval for Partitioned Document Collections. Current information retrieval services make use of massive indexes in order to resolve content-based queries. Monolithic approaches like this have been effective until now because the volume of data stored has been manageable on a single machine or tightly-coupled cluster of machines, and because the data has been available for collection. But with an increasing amount of automatically generated data, and an increasing diversity of information sources, other approaches are required. In this project we will investigate mechanisms for handling retrieval tasks when the indexes to the data are stored locally with the data, and when no central index is viable.Read moreRead less
Building a Prototype for Quality Information Retrieval from the Internet. This projects aims to consider fundamental issues in implementing a prototype quality information retrieval system from the world wide web. These issues include: retrieval of relevant pages to a given topic using a focussed crawler; text categorisation of the retrieved web pages; quality selection criteria with the formulation of a quality index to determine quality of retrieved pages, and user interface design to obtain r ....Building a Prototype for Quality Information Retrieval from the Internet. This projects aims to consider fundamental issues in implementing a prototype quality information retrieval system from the world wide web. These issues include: retrieval of relevant pages to a given topic using a focussed crawler; text categorisation of the retrieved web pages; quality selection criteria with the formulation of a quality index to determine quality of retrieved pages, and user interface design to obtain relevance feedback. The outcome will be a prototype system useful to business and industry though to guide our thinking we will use issues in constructing a quality digital library collection as a sounding board.
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