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Scaling To Very Very Large Corpora For Natural Language Disambiguation.pdf

P01-1005.pdf
Preview of Scaling to Very Very Large Corpora for Natural Language Disambiguation
🔗 Source: acl.ldc.upenn.edu
📊 Size: 47 KB
👤 Author: t-odowns
⬇️ Downloads: 169

Summary

Michele Banko and Eric Brill of Microsoft Research evaluated the performance of different learning methods on a natural language disambiguation task, confusion set disambiguation, using large amounts of labeled data. They used four machine learning algorithms: winnow, perceptron, naïve Bayes, and a memory-based learner, and trained them on a 1-billion-word corpus, which is three orders of magnitude larger than previously used corpora. The results showed that the learning curves for these algorithms are log-linear, even with large amounts of training data, and that none of the learners asymptoted in performance at the commonly employed training corpus size. The study suggests that using more training data can lead to significant improvements in accuracy, but may also increase the size of learned representations, which can be a concern for applications where space is limited.

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Michele Banko and Eric Brill of Microsoft Research evaluated the performance of different learning methods on a natural language disambiguation task, confusion...

Technical Information

  • File Format: PDF
  • File Size: 47 KB
  • Pages: 8
  • Language: EN
  • Author: t-odowns
  • Total Downloads: 169
  • Last Updated: 1 month ago

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