Lapata, Mirella and Frank Keller. 2005. Web-based Models for Natural Language Processing. ACM Transactions on Speech and Language Processing 2:1, 1-31. 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.
@Article{Lapata:Keller:05, author = {Mirella Lapata and Frank Keller}, title = {Web-based Models for Natural Language Processing}, journal = {ACM Transactions on Speech and Language Processing}, volume = 2, issue = 1, pages = {1--31}, year = 2005 }
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