Representing Sentence Structure in Hidden Markov Models for Information Extraction
Abstract
We study the application of Hidden Markov Mod-els (HMMs) to learning information extractors for-ary relations from free text. We propose an ap-proach to representing the grammatical structure of sentences in the states of the model. We also in-vestigate using an objective function during HMM training which maximizes the ability of the learned models to identify the phrases of interest. We eval-uate our methods by deriving extractors for two bi-nary relations in biomedical domains. Our experi-ments indicate that our approach learns more accu-rate models than several baseline approaches. 1
Cite
Text
Ray and Craven. "Representing Sentence Structure in Hidden Markov Models for Information Extraction." International Joint Conference on Artificial Intelligence, 2001.Markdown
[Ray and Craven. "Representing Sentence Structure in Hidden Markov Models for Information Extraction." International Joint Conference on Artificial Intelligence, 2001.](https://mlanthology.org/ijcai/2001/ray2001ijcai-representing/)BibTeX
@inproceedings{ray2001ijcai-representing,
title = {{Representing Sentence Structure in Hidden Markov Models for Information Extraction}},
author = {Ray, Soumya and Craven, Mark},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2001},
pages = {1273-1279},
url = {https://mlanthology.org/ijcai/2001/ray2001ijcai-representing/}
}