Automatic Labelling of References for Internet Information Systems
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Today users of Internet information services like e.g. Yahoo! or AltaVista often experience high search costs. One important reason for this is the necessity to browse long reference lists manually, because of the well-known problems of relevance ranking. A possible remedy is to complement the references with automatically generated labels which provide valuable information about the referenced information source. Presenting suitably labelled lists of references to users aims at improving the clarity and thus comprehensibility of the information offered and at reducing the search cost. In the following we survey several dimensions for labelling (time, frequency of usage, region, language, subject, industry, and preferences) and the corresponding classification problems. To solve these problems automatically we sketch for each problem a pragmatic mix of machine learning methods and report selected results.
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