Bayesian Information Extraction Network
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 421-425.
Abstract
Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various aspects of language in one model. Many existing algorithms developed for learning and inference in DBNs are applicable to probabilistic language modeling. To demonstrate the potential ofDBNs for natural language processing, we employ a DBN in an information extraction task. We show how to assemble wealth of emerging linguistic instruments for shallow parsing, syntactic and semantic tagging, morphological decomposition, named entity recognition etc. in order to incrementally build a robust information extraction system. Our method outperforms previously published results on an established benchmark domain.
@inproceedings{ijcai2003p63,
title = {Bayesian Information Extraction Network},
author = {Peshkin, Leonid and Pfeffer, Avi},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {421--425},
year = {2003},
url = {https://ijcai.org/proceedings/2003/63},
}
TY - CPAPER TI - Bayesian Information Extraction Network AU - Peshkin, Leonid AU - Pfeffer, Avi T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 421 EP - 425 UR - https://ijcai.org/proceedings/2003/63 ER -Copied
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