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ISBN 10: 1484283910 ISBN 13: 9781484283912
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Language: English
Published by VDM Verlag Dr. Müller, 2011
ISBN 10: 3639292707 ISBN 13: 9783639292701
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Enhancing Factoid Question Answering Using Frame Semantics | How FrameNet may Improve Answer Passage Retrieval and Answer Processing Effectiveness in Natural Language Question Answering Systems | Bahadorreza Ofoghi | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639292701 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Seller: liu xing, Nanjing, JS, China
paperback. Condition: New. Paperback. Pub Date: 2020-05-01 Pages: 228 Publisher: Commercial Press Natural Language Processing (NLP) is an artificial intelligence (AI) is an important application area. is the second image and voice processing technologies (sensing technology) Another breakthrough a central part of exciting concerned with cognitive technology. This book question and answer form. standing on the perspective of the application of artificial intelligence. deep .
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Seller: Biblios, Frankfurt am main, HESSE, Germany
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Integrates question answering systems with document repositories from different sourcesContains an in-depth explanation of the technology behind BERTTakes a step-by-step approach to building ques.
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT.What You Will Learn Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is ForAI and machine learning developers and natural language processing developers.