Maximum Entropy Markov Models for Information Extraction and Segmentation. McCallum, A., Freitag, D., & Pereira, F. In *Proc 17th International Conf on Machine Learning*, volume 3, pages 591-598, 2000. Morgan Kaufmann, San Francisco, CA.

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Website abstract bibtex

Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually mod- eled as multinomial distributions over a discrete vocabulary, and the HMM parameters are set to maximize the likelihood of the observations. This paper presents a new Markovian sequence model, closely related to HMMs, that allows ob- servations to be represented as arbitrary overlap- ping features (such as word, capitalization, for- matting, part-of-speech), and de nes the condi- tional probability of state sequences given ob- servation sequences. It does this by using the maximum entropy framework to t a set of expo- nential models that represent the probability of a state given an observation and the previous state. We present positive experimental results on the segmentation of FAQ s.

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