1. A method for real time identification of topics in text, comprising the steps of: forming a battery of topics from training data; detecting topic changes in said text, using said battery and a first threshold ratio; identifying topics in said text, using said battery and a second threshold ratio; wherein said detecting and identifying steps are applied to said text in real time, wherein said first and second threshold ratios compare a metric having a likelihood measure, and wherein a topic can be identified for a current segment of said text prior to detecting a topic change in said segment.
1. A method for real time identification of topics in text, comprising the steps of: forming a battery of topics from training data; detecting topic changes in said text, using said battery and a first threshold ratio; identifying topics in said text, using said battery and a second threshold ratio; wherein said detecting and identifying steps are applied to said text in real time, wherein said first and second threshold ratios compare a metric having a likelihood measure, and wherein a topic can be identified for a current segment of said text prior to detecting a topic change in said segment. 3. The method of claim 1, wherein said first and second threshold ratios are estimated theoretically.