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Linguistically Motivated Statistical Machine Translation: Models and Algorithms de Deyi Xiong

Linguistically Motivated Statistical Machine Translation: Models and Algorithms

Autor Deyi Xiong, Min Zhang
en Limba Engleză Hardback – 16 mar 2015
This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform.
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Specificații

ISBN-13: 9789812873552
ISBN-10: 9812873554
Pagini: 180
Ilustrații: XII, 152 p. 52 illus.
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.41 kg
Ediția:2015
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore

Public țintă

Research

Cuprins

1 Introduction.- 2 BTG-Based SMT.- 3 Syntactically Annotated Reordering.- 4 Semantically Informed Reordering.- 5 Lexicalized Bracketing.- 6 Linguistically Motivated Bracketing.- 7 Translation Rule Selection with Document-Level Semantic Information.- 8 Translation Error Detection with Linguistic Features.- 9 Closing Remarks.- Index.- References.

Recenzii

“Linguistically Motivated Statistical Machine Translation, written by Deyi Xiong and Min Zhang is an overview of (mostly) already published work by the same researchers, rewritten into a coherent book that explains how several different research aspects fit into one research paradigm. … the book is inspiring and worth reading, if you wish to try out and improve your SMT system.” (Vincent Vandeghinste, Machine Translation, Vol. 29, 2015)

Notă biografică

Deyi Xiong is a professor at Soochow University. Previously he was a research scientist at the Institute for Infocomm Research of Singapore from 2007-2013. He completed his Ph.D. in Computer Science at the Institute of Computing Technology of Chinese Academy of Sciences in 2007. His research interests are in the area of natural language processing, including parsing and statistical machine translation.
Min Zhang is a professor at Soochow University. He obtained his Ph.D. degree in Computer Science at Harbin Institute of Technology in 1997. His research interests include machine translation, natural language processing and text mining.

Textul de pe ultima copertă

This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform.
Deyi Xiong is a professor at Soochow University. Previously he was a research scientist at the Institute for Infocomm Research of Singapore from 2007-2013. He completed his Ph.D. in Computer Science at the Institute of Computing Technology of Chinese Academy of Sciences in 2007. His research interests are in the area of natural language processing, including parsing and statistical machine translation.
Min Zhang is a professor at Soochow University. He obtained his Ph.D. degree in Computer Science at Harbin Institute of Technology in 1997. His research interests include machine translation, natural language processing and text mining.

Caracteristici

Provides solutions for open problems concerning the integration of linguistic knowledge into SMT Helps readers to better understand the effects and impacts of linguistic knowledge on machine translation Promotes the often-ignored bracketing model (or phrase segmentation model) to the SMT community Elaborates on the entire framework of BTG-based SMT formalism Includes supplementary material: sn.pub/extras