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Information Extraction with HMM Structures Learned by Stochastic Optimization (2001)
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BibTeX
@MISC{Freitag01informationextraction,
author = {Dayne Freitag and Andrew McCallum},
title = {Information Extraction with HMM Structures Learned by Stochastic Optimization},
year = {2001}
}
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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 algorithm begins with a simple model and then performs hill-climbing in the space of possible structures by splitting states and gauging performance on a validation set. Experimental results show that this technique finds HMM models that almost always out-perform a fixed model, and have superior average performance across tasks.
Keyphrases
information extraction stochastic optimization hmm structure good structure database slot simple model recent research possible structure state-transition structure superior average performance hmm model text document fixed model hidden markov model extraction accuracy experimental result strong performance
