Representing Sentence Structure in Hidden Markov Models for Information Extraction
Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence. Pages 1273-1278.
Abstract
We study the application of Hidden Markov Models (HMMs) to learning information extractors for -ary relations from free text. We propose an approach to representing the grammatical structure of sentences in the states of the model. We also investigate using an objective function during HMM training which maximizes the ability of the learned models to identify the phrases of interest. We evaluate our methods by deriving extractors for two binary relations in biomedical domains. Our experiments indicate that our approach learns more accurate models than several baseline approaches.
@inproceedings{ijcai2001p167,
title = {Representing Sentence Structure in Hidden Markov Models for Information Extraction},
author = {Ray, Soumya and Craven, Mark},
booktitle = {Proceedings of the Seventeenth International Joint Conference on
Artificial Intelligence, {IJCAI-01}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1273--1278},
year = {2001},
url = {https://ijcai.org/proceedings/2001/167},
}
TY - CPAPER TI - Representing Sentence Structure in Hidden Markov Models for Information Extraction AU - Ray, Soumya AU - Craven, Mark T2 - Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01 PB - International Joint Conferences on Artificial Intelligence PY - 2001 SP - 1273 EP - 1278 UR - https://ijcai.org/proceedings/2001/167 ER -Copied
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