A Practical Guide to Hybrid Natural Language Processing : Combining Neural Models and Knowledge Graphs for NLP / by Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva.

Format:

Book

Author/Creator:

Gómez-Pérez, José Manuel, author.

Denaux, Ronald, author.

Garcia-Silva, Andres, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Application software.

Natural language processing (Computer science).

Artificial Intelligence.

Information Systems Applications (incl. Internet).

Natural Language Processing (NLP).

Local Subjects:

Artificial Intelligence.

Information Systems Applications (incl. Internet).

Natural Language Processing (NLP).

Physical Description:

1 online resource (XXV, 268 pages) : 376 illustrations, 9 illustrations in color

Edition:

First edition 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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.

Contents:

Part I: Preliminaries and Building Blocks

Hybrid Natural Language Processing: An Introduction

Word, Sense, and Graph Embeddings

UnderstandingWord Embeddings and Language Models

Capturing Meaning from Text asWord Embeddings

Capturing Knowledge Graph Embeddings

Part II: Combining Neural Architectures and Knowledge Graphs

Building Hybrid Representations from Text Corpora, Knowledge Graphs, and Language Models

Quality Evaluation

Capturing Lexical, Grammatical, and Semantic Information with Vecsigrafo

Aligning Embedding Spaces and Applications for Knowledge Graphs

Part III: Applications

A Hybrid Approach to Disinformation Analysis

Jointly Learning Text and Visual Information in the Scientific Domain

Looking into the Future of Natural Language Processing.

Other Format:

Printed edition:

ISBN:

978-3-030-44830-1

9783030448301

Access Restriction:

Restricted for use by site license.