Hierarchical Semantic Classification: Word Sense Disambiguation with World Knowledge
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 817-821.
Abstract
We present a learning architecture for lexical semantic classification problems that supplements task-specific training data with background data encoding general "world knowledge". The model compiles knowledge contained in a dictionaryontology into additional training data, and integrates task-specific and background data through a novel hierarchical learning architecture. Experiments on a word sense disambiguation task provide empirical evidence that this "hierarchical classifier" outperforms a state-of-the-art standard "flat" one.
@inproceedings{ijcai2003p118,
title = {Hierarchical Semantic Classification: Word Sense Disambiguation with World Knowledge},
author = {Ciaramita, Massimiliano and Hofmann, Thomas and Johnson, Mark},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {817--821},
year = {2003},
url = {https://ijcai.org/proceedings/2003/118},
}
TY - CPAPER TI - Hierarchical Semantic Classification: Word Sense Disambiguation with World Knowledge AU - Ciaramita, Massimiliano AU - Hofmann, Thomas AU - Johnson, Mark T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 817 EP - 821 UR - https://ijcai.org/proceedings/2003/118 ER -Copied
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