Web-based models for natural language processing

  • Authors:

  • Mirella Lapata;Frank Keller

  • Affiliations:

  • University of Edinburgh, Edinburgh, UK;University of Edinburgh, Edinburgh, UK

  • Venue:
  • ACM Transactions on Speech and Language Processing (TSLP)
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Previous work demonstrated that Web counts can be used to approximate bigram counts, suggesting that Web-based frequencies should be useful for a wide variety of Natural Language Processing (NLP) tasks. However, only a limited number of tasks have so far been tested using Web-scale data sets. The present article 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.