Extracting the Names of Genes and Gene Products with a Hidden Markov Model (2000)
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BibTeX
@INPROCEEDINGS{Collier00extractingthe,
author = {Nigel Collier and Chikashi Nobata and Jun-ichi Tsujii},
title = {Extracting the Names of Genes and Gene Products with a Hidden Markov Model},
booktitle = {},
year = {2000},
pages = {201--207}
}
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Abstract
We report the results of a study into the use of a linear interpolating hidden Markov model (HMM) for the task of extracting technical ter- minology from MEDLINE abstracts and texts in the molecular-biology domain. This is the first stage in a system that will extract event information for automatically updating biology databases. We trained the HMM entirely with bigrams based on lexical and character features in a relatively small corpus of 100 MEDLINE abstracts that were marked-up by domain experts with term classes such as proteins and DNA. Using cross-validation methods we achieved an F-score of 0.73 and we examine the contribution made by each part of the interpolation model to overcoming data sparseness.
