Using Web Search For Machine Translation
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Using Web Search For Machine Translation
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Author : Nicolas Wehmeier
language : en
Publisher:
Release Date : 2004
Using Web Search For Machine Translation written by Nicolas Wehmeier and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Translation And Web Searching
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Author : Vanessa Enríquez Raído
language : en
Publisher: Routledge
Release Date : 2013-11-07
Translation And Web Searching written by Vanessa Enríquez Raído and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-07 with Language Arts & Disciplines categories.
The book presents a comprehensive study of various cognitive and affective aspects of web searching for translation problem solving. Research into the use of the web as an external aid of consultation has frequently occupied a secondary position in the investigation of translation processes. The book aims to bridge this gap in the literature. Beginning with a detailed survey of previous studies of these processes, it then focuses on web search behaviors using qualitative and quantitative analysis that presents a multifaceted overview of translation-oriented web searching. The book concludes by addressing the implications for the teaching of and research into translators’ web searching skills. With regard to teaching, the book's didactic discussions will make it a valuable tool for both translator trainers and translation students wanting to familiarize themselves with the intricacies of Web searching and to reflect upon the pedagogical implications of the study for acquiring online information literacy in translator training.
Navigating The Web
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Author : Claire Y. Shih
language : en
Publisher: Cambridge University Press
Release Date : 2023-04-13
Navigating The Web written by Claire Y. Shih and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-13 with Language Arts & Disciplines categories.
This Element reports an investigation of translators' use of web-based resources and search engines. The study adopted a qualitative eye tracking-based methodology utilising a combination of gaze replay and retrospective think aloud (RTA) to elicit data. The main contribution of this Element lies in presenting not only an alternative eye tracking methodology for investigating translators' web search behaviour but also a systematic approach to gauging the reasoning behind translators' highly complex and context-dependent interaction with search engines and the Web.
Electronic Tools For Translators
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Author : Frank Austermuhl
language : en
Publisher: Routledge
Release Date : 2014-05-01
Electronic Tools For Translators written by Frank Austermuhl and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-01 with Language Arts & Disciplines categories.
Electronic Tools for Translators offers complete explanations of a wide range of software products, information resources and online services that translators now need to understand and use. Individual chapters run through the origins and nature of the internet, the many ways of searching for information, and translation resources on the web, CD-ROMs as information sources, computer-assisted terminology management, the use and construction of corpora, translation memories, localization tools, and the incorporation of machine translation programmes into the translation process. Austermühl explains all these tools and resources in a clear, step-by-step way, suggesting learning tasks and activities for each chapter and guiding the reader through the jargon. Examples are drawn from English, French, German and Spanish. The book can be used as a text in regular classes on computer-assisted translation, in translation practice classes, as well as for self-learning by professionals wishing to update their skills.
Machine Translation And Global Research
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Author : Lynne Bowker
language : en
Publisher: Emerald Group Publishing
Release Date : 2019-05-01
Machine Translation And Global Research written by Lynne Bowker and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-01 with Computers categories.
Lynne Bowker and Jairo Buitrago Ciro introduce the concept of machine translation literacy, a new kind of literacy for scholars and librarians in the digital age. This book is a must-read for researchers and information professionals eager to maximize the global reach and impact of any form of scholarly work.
Learning To Rank For Information Retrieval And Natural Language Processing
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Author : Hang Li
language : en
Publisher: Springer Nature
Release Date : 2011-04-20
Learning To Rank For Information Retrieval And Natural Language Processing written by Hang Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-20 with Computers categories.
Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting SVM, Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Introduction / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work
The Role Of Online Machine Translation In Webpage Translation
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Author : Federico Gaspari
language : en
Publisher:
Release Date : 2010
The Role Of Online Machine Translation In Webpage Translation written by Federico Gaspari and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
Deep Learning For Search
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Author : Tommaso Teofili
language : en
Publisher: Simon and Schuster
Release Date : 2019-06-02
Deep Learning For Search written by Tommaso Teofili and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-02 with Computers categories.
Summary Deep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. By the time you're finished with the book, you'll be ready to build amazing search engines that deliver the results your users need and that get better as time goes on! Foreword by Chris Mattmann. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning handles the toughest search challenges, including imprecise search terms, badly indexed data, and retrieving images with minimal metadata. And with modern tools like DL4J and TensorFlow, you can apply powerful DL techniques without a deep background in data science or natural language processing (NLP). This book will show you how. About the Book Deep Learning for Search teaches you to improve your search results with neural networks. You'll review how DL relates to search basics like indexing and ranking. Then, you'll walk through in-depth examples to upgrade your search with DL techniques using Apache Lucene and Deeplearning4j. As the book progresses, you'll explore advanced topics like searching through images, translating user queries, and designing search engines that improve as they learn! What's inside Accurate and relevant rankings Searching across languages Content-based image search Search with recommendations About the Reader For developers comfortable with Java or a similar language and search basics. No experience with deep learning or NLP needed. About the Author Tommaso Teofili is a software engineer with a passion for open source and machine learning. As a member of the Apache Software Foundation, he contributes to a number of open source projects, ranging from topics like information retrieval (such as Lucene and Solr) to natural language processing and machine translation (including OpenNLP, Joshua, and UIMA). He currently works at Adobe, developing search and indexing infrastructure components, and researching the areas of natural language processing, information retrieval, and deep learning. He has presented search and machine learning talks at conferences including BerlinBuzzwords, International Conference on Computational Science, ApacheCon, EclipseCon, and others. You can find him on Twitter at @tteofili. Table of Contents PART 1 - SEARCH MEETS DEEP LEARNING Neural search Generating synonyms PART 2 - THROWING NEURAL NETS AT A SEARCH ENGINE From plain retrieval to text generation More-sensitive query suggestions Ranking search results with word embeddings Document embeddings for rankings and recommendations PART 3 - ONE STEP BEYOND Searching across languages Content-based image search A peek at performance
Learning To Rank For Information Retrieval And Natural Language Processing Second Edition
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Author : Hang Li
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Learning To Rank For Information Retrieval And Natural Language Processing Second Edition written by Hang Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work
Envisioning Machine Translation In The Information Future
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Author : John S. White
language : en
Publisher: Springer
Release Date : 2003-07-31
Envisioning Machine Translation In The Information Future written by John S. White and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-31 with Computers categories.
Envisioning Machine Translation in the Information Future When the organizing committee of AMTA-2000 began planning, it was in that brief moment in history when we were absorbed in contemplation of the passing of the century and the millennium. Nearly everyone was comparing lists of the most important accomplishments and people of the last 10, 100, or 1000 years, imagining the radical changes likely over just the next few years, and at least mildly anxious about the potential Y2K apocalypse. The millennial theme for the conference, “Envisioning MT in the Information Future,” arose from this period. The year 2000 has now come, and nothing terrible has happened (yet) to our electronic infrastructure. Our musings about great people and events probably did not ennoble us much, and whatever sense of jubilee we held has since dissipated. So it may seem a bit obsolete or anachronistic to cast this AMTA conference into visionary themes.