Word Sense Disambiguation with Distribution Estimation
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1010-1014.
Abstract
A word sense disambiguation (WSD) system trained on one domain and applied to a different domain will show a decrease in performance. One major reason is the different sense distributions between different domains. This paper presents novel application of two distribution estimation algorithms to provide estimates of the sense distribution of the new domain data set. Even though our training examples are automatically gathered from parallel corpora, the sense distributions estimated are good enough to achieve a relative improvement of 56% when incorporated into our WSD system.
@inproceedings{ijcai2005p202,
title = {Word Sense Disambiguation with Distribution Estimation},
author = {Chan, Yee Seng and Ng, Hwee Tou},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1010--1014},
year = {2005},
url = {https://ijcai.org/proceedings/2005/202},
}
TY - CPAPER TI - Word Sense Disambiguation with Distribution Estimation AU - Chan, Yee Seng AU - Ng, Hwee Tou T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1010 EP - 1014 UR - https://ijcai.org/proceedings/2005/202 ER -Copied
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