Optimizing Classifier Performance in Word Sense Disambiguation by Redefining Sense Classes
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1635-1639.
Abstract
Learning word sense classes has been shown to be useful in fine-grained word sense disambiguation. However, the common choice for sense classes, WordNet lexicographer files, are not designed for machine learning based word sense disambiguation. In this work, we explore the use of clustering techniques in an effort to construct sense classes that are more suitable for word sense disambiguation end-task. Our results show that these classes can significantly improve classifier performance over the state of the art results of unrestricted word sense disambiguation.
@inproceedings{ijcai2007p264,
title = {Optimizing Classifier Performance in Word Sense Disambiguation by Redefining Sense Classes},
author = {Kohomban, Upali Sathyajith and Lee, Wee Sun},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1635--1639},
year = {2007},
url = {https://ijcai.org/proceedings/2007/264},
}
TY - CPAPER TI - Optimizing Classifier Performance in Word Sense Disambiguation by Redefining Sense Classes AU - Kohomban, Upali Sathyajith AU - Lee, Wee Sun T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1635 EP - 1639 UR - https://ijcai.org/proceedings/2007/264 ER -Copied
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