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Natural Language Processing Using Very Large Corpora

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Cover of 'Natural Language Processing Using Very Large Corpora'

Table of Contents

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    Book Overview
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    Chapter 1 Implementation and Evaluation of a German HMM for POS Disambiguation
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    Chapter 2 Improvements in Part-of-Speech Tagging with an Application to German
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    Chapter 3 Unsupervised Learning of Disambiguation Rules for Part-of-Speech Tagging
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    Chapter 4 Tagging French without Lexical Probabilities — Combining Linguistic Knowledge and Statistical Learning
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    Chapter 5 Example-Based Sense Tagging of Running Chinese Text
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    Chapter 6 Disambiguating Noun Groupings with Respect to WordNet Senses
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    Chapter 7 A Comparison of Corpus-Based Techniques for Restoring Accents in Spanish and French Text
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    Chapter 8 Beyond Word N -Grams
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    Chapter 9 Statistical Augmentation of a Chinese Machine-Readable Dictionary
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    Chapter 10 Text Chunking Using Transformation-Based Learning
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    Chapter 11 Prepositional Phrase Attachment Through a Backed-off Model
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    Chapter 12 On the Unsupervised Induction of Phrase-Structure Grammars
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    Chapter 13 Robust Bilingual Word Alignment for Machine Aided Translation
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    Chapter 14 Iterative Alignment of Syntactic Structures for a Bilingual Corpus
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    Chapter 15 Trainable Coarse Bilingual Grammars for Parallel Text Bracketing
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    Chapter 16 Comparative Discourse Analysis of Parallel Texts
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    Chapter 17 Comparing the Retrieval Performance of English and Japanese Text Databases
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    Chapter 18 Inverse Document Frequency (IDF): A Measure of Deviations from Poisson
Attention for Chapter 3: Unsupervised Learning of Disambiguation Rules for Part-of-Speech Tagging
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Chapter title
Unsupervised Learning of Disambiguation Rules for Part-of-Speech Tagging
Chapter number 3
Book title
Natural Language Processing Using Very Large Corpora
Published by
Springer, Dordrecht, January 1999
DOI 10.1007/978-94-017-2390-9_3
Book ISBNs
978-9-04-815349-7, 978-9-40-172390-9
Authors

E. Brill, M. Pop

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 2%
United States 3 2%
Spain 3 2%
Uganda 1 <1%
Netherlands 1 <1%
France 1 <1%
Hungary 1 <1%
United Kingdom 1 <1%
Malaysia 1 <1%
Other 2 1%
Unknown 132 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 19%
Researcher 21 14%
Student > Master 21 14%
Professor > Associate Professor 13 9%
Professor 8 5%
Other 23 15%
Unknown 35 23%
Readers by discipline Count As %
Computer Science 74 50%
Linguistics 16 11%
Engineering 6 4%
Social Sciences 3 2%
Agricultural and Biological Sciences 2 1%
Other 11 7%
Unknown 37 25%