Applications of Machine Learning to Information Access

Mehran Sahami

The recent explosion of on-line information has given rise to a number of query-based search engines (e.g., Alta Vista) and manually constructed topic hierarchies (e.g., Yuhoo!). But with the current rate of growth in the amount of available information, query results grow incomprehensibly large and manual classification in topic hierarchies creates an immense information bottleneck. Therefore, these tools are rapidly becoming inadequate for addressing users’ information needs. We address these problems with a system for topical information space navigation that combines the query-based and taxonomic systems. Our system is built within a unifying probabilistic framework, thereby harnessing the expressive power of this representation while also providing understandable semantics.


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