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Maximum Entropy Markov Models for Information Extraction and Segmentation (2000) [263 citations — 15 self]

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@INPROCEEDINGS{Mccallum00maximumentropy,
    author = {Andrew Mccallum and Dayne Freitag and Fernando Pereira},
    title = {Maximum Entropy Markov Models for Information Extraction and Segmentation},
    booktitle = {},
    year = {2000},
    pages = {591--598},
    publisher = {Morgan Kaufmann}
}

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Abstract:

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 modeled 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 observations to be represented as arbitrary overlapping features (such as word, capitalization, formatting, part-of-speech), and defines the conditional probability of state sequences given observation sequences. It does this by using the maximum entropy framework to fit a set of exponential 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.

Citations

2397	A tutorial on hidden markov models and selected applications in speech recognition – Rabiner - 1989
367	Inducing features of random fields – Pietra, Pietra, et al. - 1997
296	Generalized iterative scaling for log-linear models – Darroch, Ratcliff - 1972
272	The infinite Hidden Markov Model – Beal, Ghahramani, et al. - 2003
220	An algorithm that learns what’s in a name – Bikel, Schwartz, et al.
150	Robust part-of-speech tagging using a hidden markov model. Computer speech and language – Kupiec - 1992
142	Adaptive Statistical Language Modeling: A Maximum Entropy Approach – Rosenfeld - 1994
138	A Gaussian prior for smoothing maximum entropy model – Chen, Rosenfeld - 1999
137	Statistical models for text segmentation – Beeferman, Berger, et al. - 1999
129	Maximum Entropy Models for Natural Language Ambiguity Resolution – Ratnaparkhi - 1998
126	Stochastic simulation algorithms for dynamic probabilistic networks – Kanazawa, Koller, et al. - 1995
99	Introduction to probabilistic automata – Paz - 1971
74	Information extraction using hidden Markov models – Leek - 1997
73	Exploiting diverse knowledge sources via maximum entropy in named entity recognition – Borthwiek, Sterling, et al. - 1998
37	question answering from Frequently-Asked Question Files – Burke, Hammond, et al. - 1997
24	Information extraction using hmms and shrinkage – Freitag, McCallum - 1999
18	Efficient sampling and feature selection in whole sentence maximum entropy language models – Chen, Rosenfeld - 1999
6	Markov processes on curves for automatic speech recognition – Saul, Rahim - 1999