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Bursty and Hierarchical Structure in Streams.

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The high-level idea is to analyze a stream of documents and find features whose behavior is `bursty’: they occur with high intensity over a limited period of time. The analysis uses a probabilistic automaton whose states correspond to the frequencies of individual words. For each word separately, one imagines a copy of the automaton generating occurrences of the word, and computes its most likely state sequence over the course of the stream. State transitions correspond to points in time around which the frequency of the word changes significantly—that is, to the beginning or end of a `burst’ in the usage of the word. The full details of all this are given in the paper here (also have a look at some of the results )

This talk is part of the Machine Learning Journal Club series.

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