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Abstract: Recent research has demonstrated the strong performance of hidden Markov models applied to information extraction -- the task of populating database slots with corresponding phrases from text documents. A remaining problem, however, is the selection of state-transition structure for the model. This paper demonstrates that extraction accuracy strongly depends on the selection of structure, and presents an algorithm for automatically finding good structures by stochastic optimization. Our... (Update)
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BibTeX entry: (Update)
Freitag, D., & McCallum, A. (2000). Information extraction with HMM structures learned by stochastic optimization. Proceedings of the Eighteenth Conference on Artificial Intelligence (AAAI-2000). http://citeseer.ist.psu.edu/freitag00information.html More
@inproceedings{ freitag00information,
author = "Dayne Freitag and Andrew McCallum",
title = "Information Extraction with {HMM} Structures Learned by Stochastic Optimization",
booktitle = "{AAAI}/{IAAI}",
pages = "584-589",
year = "2000",
url = "citeseer.ist.psu.edu/freitag00information.html" }
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