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🔗 Source: microsoft.com
📊 Size: 49 KB
👤 Author: t-odowns
⬇️ Downloads: 309

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 found that the learning curves for these algorithms were log-linear, even with up to one billion words of training data. The results suggest that using more data can lead to significant improvements in performance, but may also increase the size of the learned representations, which can be a concern for some applications. The study also considered methods for effectively exploiting very large corpora when labeled data comes at a cost, including voting, sample selection, and partially unsupervised learning.

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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: 49 KB
  • Pages: 8
  • Language: EN
  • Author: t-odowns
  • Total Downloads: 309
  • Last Updated: 1 month ago

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