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A number of emerging large scale applications such as web archiving and time-stamped web objects generated through information feeds involve time-evolving objects that can be most effectively explored through search within a temporal context. The authors develop in this paper a new approach to handle the temporal text search of a time evolving collection of documents. Specifically, given a temporally anchored query, the method will return a ranked set of documents that were live during the query time span and the relevance scores are computed relative to the state of the collection as it existed during the query time span.
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