Semisupervised Learning for Computational Linguistics

Semisupervised Learning for Computational Linguistics
Author: Steven Abney
Publisher: CRC Press
Total Pages: 322
Release: 2007-09-17
Genre: Business & Economics
ISBN: 1420010808

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The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspecialists to keep up to date in the field. Providing a broad, accessible treatment of the theory as well as linguistic applications, Semisupervised Learning for Computational Linguistics offer


Semisupervised Learning for Computational Linguistics
Language: en
Pages: 322
Authors: Steven Abney
Categories: Business & Economics
Type: BOOK - Published: 2007-09-17 - Publisher: CRC Press

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The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspec
Explainable Natural Language Processing
Language: en
Pages: 107
Authors: Anders Søgaard
Categories: Computers
Type: BOOK - Published: 2022-06-01 - Publisher: Springer Nature

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This book presents a taxonomy framework and survey of methods relevant to explaining the decisions and analyzing the inner workings of Natural Language Processi
Semi-supervised Learning for Natural Language Processing
Language: en
Pages:
Authors:
Categories: Computational linguistics
Type: BOOK - Published: 2009 - Publisher:

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Semi-Supervised Learning
Language: en
Pages: 525
Authors: Olivier Chapelle
Categories: Computers
Type: BOOK - Published: 2010-01-22 - Publisher: MIT Press

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A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxon
Semi-Supervised Learning and Domain Adaptation in Natural Language Processing
Language: en
Pages: 93
Authors: Anders Søgaard
Categories: Computers
Type: BOOK - Published: 2022-05-31 - Publisher: Springer Nature

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This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can