Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Machine Translation: An Introductory Guide
🌠 Top Free Web Programming Books - 100% Free or Open Source!
  • Title: Machine Translation: An Introductory Guide
  • Author(s) D. Arnold, L. Balkan, R. Lee Humphreys, S. Meijer, L. Sadler
  • Publisher: Blackwell Pub;
  • Paperback: 200 pages
  • eBook: HTML, PostScript, and PDF (234 pages, 1.3 MB)
  • Language: English
  • ISBN-10: 185554217X
  • ISBN-13: 978-1855542174
  • Share This:  

Book Description

This introductory book looks at all aspects of Machine Translation: covering questions of what it is like to use a modern Machine Translation system, through questions about how it is done, to questions of evaluating systems, and what developments can be foreseen in the near to medium future.

It then presents new or improved statistical Machine Translation techniques, including a discriminative training framework for leveraging syntactic information, the use of semi-supervised and kernel-based learning methods, and the combination of multiple Machine Translation outputs in order to improve overall translation quality.

About the Authors
  • N/A
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Machine Translation for Everyone: Age of Artificial Intelligence

    This book presents a rationale for learning about Machine Translation (MT), and provides both a basic introduction to contemporary machine-learning based MT, and a more advanced discussion of neural MT.

  • Language Translation Using PCCTS and C++: A Reference Guide

    This book is a reference guide for the parser generator ANTLR, ANother Tool for Language Recognition, and the tree-parser generator SORCERER, which is suited to source-to-source translation.

  • Speech and Language Processing (Dan Jurafsky, et al)

    This text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. It describes a unified vision of speech and language processing. Emphasis is on practical and scientific applications.

  • Foundations of Machine Learning (Mehryar Mohri, et al)

    This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

  • Dive into Deep Learning (Aston Zhang, et al.)

    This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

  • O'Reilly® Natural Language Processing with Python

    This book offers a highly accessible introduction to natural language processing (NLP), the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.

  • Natural Language Processing Succinctly (Joseph Booth)

    This book will guide readers through designing a simple system that can interpret and provide reasonable responses to written English text. With this foundation, readers will be prepared to tackle the greater challenges of natural language development.

  • NLP - Skills for Learning (Peter Freeth)

    This is a book about the application of NLP (Neuro Linguistic Programming) in teaching, training and education. It is a book about NLP for trainers and a general introduction to NLP - all in one.

  • Natural Language Processing for the Working Programmer

    This book is a guide to the wonderful world of language processing for the practical working programmer, using Haskell. It is about that type of information - techniques that so-called computational linguists use to analyze the structure of human language.

  • Natural Language Processing for Prolog Programmers

    An examination of natural language processing in Prolog for those who know Prolog but not linguistics, this book enables students to move quickly into writing and working in useful software for natural language processing (NLP).

  • Prolog and Natural-Language Analysis (Fernando Pereira, et al)

    A concise and practical introduction to logic programming and the language Prolog both as vehicles for understanding elementary computational linguistics and as tools for implementing the basic components of natural-language-processing (NLP) systems.

  • Natural Language Processing Techniques in Prolog

    This book introduces the subject through the discussion and development of various computer programs which illustrate some of the basic concepts and techniques of natural language processing (NLP).

  • Hands-On Natural Language Processing with Python

    It teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today's NLP challenges. you will be well versed in building deep learning-backed NLP apps, along with overcoming NLP challenges.

Book Categories
:
Other Categories
Resources and Links