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Title:Web-based Models for Natural Language Processing
Authors: Mirella Lapata ; Frank Keller
Date: 2005
Publication Title:ACM Transactions on Speech and Language Processing
Publication Type:Journal Article Publication Status:Published
Volume No:# 2(1)
DOI:10.1145/1075389.1075392 ISBN/ISSN:15504875
Previous work demonstrated that web counts can be used to approximate bigram counts, thus suggesting that web-based frequencies should be useful for a wide variety of NLP tasks. However, only a limited number of tasks have so far been tested using web-scale data sets. The present paper overcomes this limitation by systematically investigating the performance of web-based models for several NLP tasks, covering both syntax and semantics, both generation and analysis, and a wider range of n-grams and parts of speech than have been previously explored. For the majority of our tasks, we find that simple, unsupervised models perform better when n-gram counts are obtained from the web rather than from a large corpus. In some cases, performance can be improved further by using backoff or interpolation techniques that combine web counts and corpus counts. However, unsupervised web-based models generally fail to outperform supervised state-of-the-art models trained on smaller corpora. We argue that web-based models should therefore be used as a baseline for, rather than an alternative to, standard supervised models.
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Bibtex format
author = { Mirella Lapata and Frank Keller },
title = {Web-based Models for Natural Language Processing},
journal = {ACM Transactions on Speech and Language Processing},
publisher = {ACM},
year = 2005,
volume = {# 2(1)},
doi = {10.1145/1075389.1075392},
url = {},

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