Information Extraction Using Hidden Markov Models (1997)
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BibTeX
@MISC{Leek97informationextraction,
author = {Timothy Robert Leek},
title = {Information Extraction Using Hidden Markov Models},
year = {1997}
}
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Abstract
This thesis shows how to design and tune a hidden Markov model to extract factual information from a corpus of machine-readable English prose. In particular, the thesis presents a HMM that classifies and parses natural language assertions about genes being located at particular positions on chromosomes. The facts extracted by this HMM can be inserted into biological databases. The HMM is trained on a small set of sentence fragments chosen from the collected scientific abstracts in the OMIM (On-Line Mendelian Inheritance in Man) database and judged to contain the target binary relationship between gene names and gene locations. Given a novel sentence, all contiguous fragments are ranked by log-odds score, i.e. the log of the ratio of the probability of the fragment according to the target HMM to that according to a "null" HMM trained on all OMIM sentences. The most probable path through the HMM gives bindings for the annotations with precision as high as 80%. In contrast with traditional natural language processing methods, this stochastic approach makes no use either of part-of-speech taggers or dictionaries, instead employing non-emitting states to assemble modules roughly corresponding to noun, verb, and prepostional phrases. Algorithms for reestimating parameters for HMMs with non-emitting states are presented in detail. The ability to tolerate new words and recognize a wide variety of syntactic forms arises from the judicious use of "gap" states.
