Named Entity Disambiguation at Scale

Named Entity Disambiguation (NED) is a crucial task in many Natural Language Processing applications such as entity linking, record linkage, knowledge base construction, or relation extraction, to name a few. The task in NED is to map textual variations o

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Frank-Peter Schilling Thilo Stadelmann (Eds.)

Artificial Neural Networks in Pattern Recognition 9th IAPR TC3 Workshop, ANNPR 2020 Winterthur, Switzerland, September 2–4, 2020 Proceedings

123

Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science

Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany

Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany

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More information about this series at http://www.springer.com/series/1244

Frank-Peter Schilling Thilo Stadelmann (Eds.) •

Artificial Neural Networks in Pattern Recognition 9th IAPR TC3 Workshop, ANNPR 2020 Winterthur, Switzerland, September 2–4, 2020 Proceedings

123

Editors Frank-Peter Schilling Zurich University of Applied Sciences ZHAW Winterthur, Switzerland

Thilo Stadelmann Zurich University of Applied Sciences ZHAW Winterthur, Switzerland

ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-030-58308-8 ISBN 978-3-030-58309-5 (eBook) https://doi.org/10.1007/978-3-030-58309-5 LNCS Sublibrary: SL7 – Artificial Intelligence © Springer Nature Switzerland AG 2020 Chapter “Structured (De)composable Representations Trained with Neural Networks” is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/). For further details see licence information in the chapter. 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, expressed 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. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This volume contains the papers presented at the 9th IAPR TC3 Workshop on Artificial Neural Networks for Patt