A Probabilistic Learning Method for XML Annotation of Documents
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1016-1020.
Abstract
We consider the problem of semantic annotation of semi-structured documents according to a target XML schema. The task is to annotate a document in a tree-like manner where the annotation tree is an instance of a tree class defined by DTD or W3C XML Schema descriptions. In the probabilistic setting, we cope with the tree annotation problem as a generalized probabilistic context-free parsing of an observation sequence where each observation comes with a probability distribution over terminals supplied by a probabilistic classifier associated with the content of documents. We determine the most probable tree annotation by maximizing the joint probability of selecting a terminal sequence for the observation sequence and the most probable parse for the selected terminal sequence.
@inproceedings{ijcai2005p29,
title = {A Probabilistic Learning Method for XML Annotation of Documents},
author = {Chidlovskii, Boris and Fuselier, Jérôme},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1016--1020},
year = {2005},
url = {https://ijcai.org/proceedings/2005/29},
}
TY - CPAPER TI - A Probabilistic Learning Method for XML Annotation of Documents AU - Chidlovskii, Boris AU - Fuselier, Jérôme T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1016 EP - 1020 UR - https://ijcai.org/proceedings/2005/29 ER -Copied
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