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Coarse-to-Fine Natural Language Processing by Slav Petrov (English) Paperback Bo

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Item specifics

Condition
New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
ISBN-13
9783642427497
Book Title
Coarse-to-Fine Natural Language Processing
ISBN
9783642427497

About this product

Product Identifiers

Publisher
Springer Berlin / Heidelberg
ISBN-10
3642427499
ISBN-13
9783642427497
eBay Product ID (ePID)
177300916

Product Key Features

Number of Pages
Xxii, 106 Pages
Language
English
Publication Name
Coarse-To-Fine Natural Language Processing
Publication Year
2014
Subject
Intelligence (Ai) & Semantics, General
Type
Textbook
Subject Area
Computers
Author
Slav Petrov
Series
Theory and Applications of Natural Language Processing Ser.
Format
Trade Paperback

Dimensions

Item Weight
7.4 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
1.Introduction.- 2.Latent Variable Grammars for Natural Language Parsing.- 3.Discriminative Latent Variable Grammars.- 4.Structured Acoustic Models for Speech Recognition.- 5.Coarse-to-Fine Machine Translation Decoding.- 6.Conclusions and Future Work.- Bibliography.
Synopsis
The impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions. This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing. Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. The book is intended for students and researchers interested in statistical approaches to Natural Language Processing. Slav's work Coarse-to-Fine Natural Language Processing represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach. Eugene Charniak (Brown University), 1.Introduction.- 2.Latent Variable Grammars for Natural Language Parsing.- 3.Discriminative Latent Variable Grammars.- 4.Structured Acoustic Models for Speech Recognition.- 5.Coarse-to-Fine Machine Translation Decoding.- 6.Conclusions and Future Work.- Bibliography., This book presents a coarse-to-fine framework for learning and inference in large statistical models for natural language processing. The text shows applications of this fast, accurate approach to syntactic parsing, speech recognition and machine translation.
LC Classification Number
QA75.5-76.95

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