Mining Soft-Matching Rules from Textual Data
Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence. Pages 979-985.
Abstract
Text mining concerns the discovery of knowledge from unstructured textual data. One important task is the discovery of rules that relate specific words and phrases. Although existing methods for this task learn traditional logical rules, soft-matching methods that utilize word-frequency information generally work better for textual data. This paper presents a rule induction system, T EXT RISE, that allows for partial matching of text-valued features by combining rule-based and instance-based learning. We present initial experiments applying T EXT RISE to corpora of book descriptions and patent documents retrieved from the web and compare its results to those of traditional rule and instance based methods.
@inproceedings{ijcai2001p134,
title = {Mining Soft-Matching Rules from Textual Data},
author = {Nahm, Un Yong and Mooney, Raymond J.},
booktitle = {Proceedings of the Seventeenth International Joint Conference on
Artificial Intelligence, {IJCAI-01}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {979--985},
year = {2001},
url = {https://ijcai.org/proceedings/2001/134},
}
TY - CPAPER TI - Mining Soft-Matching Rules from Textual Data AU - Nahm, Un Yong AU - Mooney, Raymond J. T2 - Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01 PB - International Joint Conferences on Artificial Intelligence PY - 2001 SP - 979 EP - 985 UR - https://ijcai.org/proceedings/2001/134 ER -Copied
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