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Inductive Semi-supervised Learning Methods: With Applicability To Natural Language Processing.pdf

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Preview of Inductive Semi-supervised Learning Methods: with Applicability to Natural Language Processing
🔗 Source: cs.sfu.ca
📊 Size: 2.72 MB
👤 Author: Anoop Sarkar
⬇️ Downloads: 303

Summary

Inductive Semi-supervised Learning with Applicability to NLP, by Anoop Sarkar and Gholamreza Haffari, covers semi-supervised learning (SSL) concepts, including introduction to SSL, classifier-based methods, generative models, and data-based methods. SSL combines supervised learning with additional unlabeled data or unsupervised learning with additional labeled data. The motivation for SSL is pragmatic, as unlabeled data is cheap to collect, and philosophical, as the brain can exploit unlabeled data. The presentation discusses why more data, including unlabeled data, can help in learning, especially in addressing the sparse data problem in NLP. It also covers preliminaries such as generative and discriminative models, including examples like Naive Bayes and SVM, and provides intuition in large-margin settings and when using the EM algorithm.

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Description

Inductive Semi-supervised Learning with Applicability to NLP, by Anoop Sarkar and Gholamreza Haffari, covers semi-supervised learning (SSL) concepts, including...

Technical Information

  • File Format: PDF
  • File Size: 2.72 MB
  • Pages: 68
  • Language: EN
  • Author: Anoop Sarkar
  • Total Downloads: 303
  • Last Updated: 2 hours ago

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This PDF document about Inductive Semi-supervised Learning Methods: with Applicability to Natural Language Processing provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Inductive Semi-supervised Learning Methods: with Applicability to Natural Language Processing.

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