Web-Scale N-gram Models for Lexical Disambiguation
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1507-1511.
Abstract
Web-scale data has been used in a diverse range of language research. Most of this research has used web counts for only short, fixed spans of context. We present a unified view of using web counts for lexical disambiguation. Unlike previous approaches, our supervised and unsupervised systems combine information from multiple and overlapping segments of context. On the tasks of preposition selection and context-sensitive spelling correction, the supervised system reduces disambiguation error by 20-24% over the current state-of-the-art.Shane Bergsma, Dekang Lin, Randy Goebel
@inproceedings{ijcai2009p252,
title = {Web-Scale N-gram Models for Lexical Disambiguation},
author = {Bergsma, Shane and Lin, Dekang and Goebel, Randy},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1507--1511},
year = {2009},
url = {https://ijcai.org/proceedings/2009/252},
}
TY - CPAPER TI - Web-Scale N-gram Models for Lexical Disambiguation AU - Bergsma, Shane AU - Lin, Dekang AU - Goebel, Randy T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1507 EP - 1511 UR - https://ijcai.org/proceedings/2009/252 ER -Copied
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