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A Practical Guide to Hybrid Natural Language Processing

Combining Neural Models and Knowledge Graphs for NLP

  • Jose Manuel Gomez-Perez
  • Ronald Denaux
  • Andres Garcia-Silva
Book

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Preliminaries and Building Blocks

    1. Front Matter
      Pages 1-1
    2. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 3-6
    3. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 7-15
    4. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 17-31
    5. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 33-39
    6. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 41-54
  3. Combining Neural Architectures and Knowledge Graphs

    1. Front Matter
      Pages 55-55
    2. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 57-89
    3. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 91-125
    4. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 127-149
    5. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 151-164
  4. Applications

    1. Front Matter
      Pages 165-165
    2. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 167-206
    3. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 207-246
    4. Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva
      Pages 247-256
  5. Back Matter
    Pages 257-268

About this book

Introduction

This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs. To this end, it first introduces the main building blocks and then describes how they can be integrated to support the effective implementation of real-world NLP applications. To illustrate the ideas described, the book also includes a comprehensive set of experiments and exercises involving different algorithms over a selection of domains and corpora in various NLP tasks.

Throughout, the authors show how to leverage complementary representations stemming from the analysis of unstructured text corpora as well as the entities and relations described explicitly in a knowledge graph, how to integrate such representations, and how to use the resulting features to effectively solve NLP tasks in a range of domains. In addition, the book offers access to executable code with examples, exercises and real-world applications in key domains, like disinformation analysis and machine reading comprehension of scientific literature. All the examples and exercises proposed in the book are available as executable Jupyter notebooks in a GitHub repository. They are all ready to be run on Google Colaboratory or, if preferred, in a local environment.

A valuable resource for anyone interested in the interplay between neural and knowledge-based approaches to NLP, this book is a useful guide for readers with a background in structured knowledge representations as well as those whose main approach to AI is fundamentally based on logic. Further, it will appeal to those whose main background is in the areas of machine and deep learning who are looking for ways to leverage structured knowledge bases to optimize results along the NLP downstream.

Keywords

Artificial Intelligence Machine Learning Semantic Web Natural Language Processing Knowledge Representation and Reasoning

Authors and affiliations

  • Jose Manuel Gomez-Perez
    • 1
  • Ronald Denaux
    • 2
  • Andres Garcia-Silva
    • 3
  1. 1.Expert SystemMadridSpain
  2. 2.Expert SystemMadridSpain
  3. 3.Expert SystemMadridSpain

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