• PDF / 9,162,253 Bytes
• 510 Pages / 504.63 x 737.01 pts Page_size
• 100 Downloads / 295 Views

DOWNLOAD

REPORT

---

Machine Learning for Text

Machine Learning for Text

Charu C. Aggarwal

Machine Learning for Text

123

Charu C. Aggarwal IBM T. J. Watson Research Center Yorktown Heights, NY, USA

ISBN 978-3-319-73530-6 ISBN 978-3-319-73531-3 (eBook) https://doi.org/10.1007/978-3-319-73531-3 Library of Congress Control Number: 2018932755 © Springer International Publishing AG, part of Springer Nature 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To my wife Lata, my daughter Sayani, and my late parents Dr. Prem Sarup and Mrs. Pushplata Aggarwal.

Preface

“ If it is true that there is always more than one way of construing a text, it is not true that all interpretations are equal.” – Paul Ricoeur The rich area of text analytics draws ideas from information retrieval, machine learning, and natural language processing. Each of these areas is an active and vibrant field in its own right, and numerous books have been written in each of these different areas. As a result, many of these books have covered some aspects of text analytics, but they have not covered all the areas that a book on learning from text is expected to cover. At this point, a need exists for a focussed book on machine learning from text. This book is a first attempt to integrate all the complexities in the areas of machine learning, information retrieval, and natural language processing in a holistic way, in order to create a coherent and integrated book in the area. Therefore, the chapters are divided into three categories: 1. Fundamental algorithms and models: Many fundamental applications in text analytics, such as matrix factorization, clustering, and classification, have u