Words in Sequence
The chapter provides an introduction to probabilistic modelling of word sequences, revealing the often predictable nature of words’ occurrences in text. Many examples are shown to understand both the strengths and weaknesses (mostly the sparse data proble
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Natural Language Understanding in a Semantic Web Context
Natural Language Understanding in a Semantic Web Context
Caroline Barrière
Natural Language Understanding in a Semantic Web Context
123
Caroline Barrière Computer Research Institute of Montreal (CRIM) Montreal Canada
ISBN 978-3-319-41335-8 DOI 10.1007/978-3-319-41337-2
ISBN 978-3-319-41337-2
(eBook)
Library of Congress Control Number: 2016943789 © Springer International Publishing Switzerland 2016 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. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
Preface
I hope for this book to serve as a good starting point for students and researchers in Semantic Web (SW) interested in discovering what Natural Language Processing (NLP) has to offer. At a time when Open Data is becoming increasingly popular, there is a pressing demand for tools to help the SW community transform those data into a shareable, normalized format, making all these data accessible as Linked Open Data. But a large portion of the data held by organizations seeking to make their data openly accessible are not stored in tables, but in much less structured forms, that is, textual forms such as reports, notes, memos, and articles. Manually generating structured information from them seems like an insurmountable task. Certainly, NLP can help uncovering the information held in text, thus augmenting the real content of the Semantic Web in a significant and lasting way. My main goal is not just to foster interest in NLP in the readership, but awareness of how useful it can be to the Semantic Web community. I will not delve very deeply into linguistic principles, but instead focus on how NLP approaches different kinds of problems and provides solutions to them. My aim is also to show how, for the past 40 years, researchers in NLP have been interested in problems closely related to the ones faced by the Seman
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