A Practical Guide to Hybrid Natural Language Processing
Home > Computing and Information Technology > Computer science > Artificial intelligence > Natural language and machine translation > A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP
A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP

A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP


     0     
5
4
3
2
1



International Edition


X
About the Book

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.

Table of 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.

About the Author :
Jose Manuel Gomez-Perez leads the Cogito Research Lab at Expert System in Madrid, Spain, where he focuses on the combination of neural and knowledge-based approaches to enable reading comprehension in machines. His work lies at the intersection of several areas of artificial intelligence, including natural language processing, knowledge graphs and deep learning. He also consults for organizations like the European Space Agency and is the co-founder of ROHub.org, a platform for the intelligent management of scientific information. A former Marie Curie fellow, José Manuel holds a Ph.D. in Computer Science and Artificial Intelligence from Universidad Politécnica de Madrid. He regularly publishes in top scientific conferences and journals and his views have appeared in magazines like Nature and Scientific American, as well as newspapers like El País. Ronald Denaux is a senior researcher scientist at Expert System. Ronald obtained his MSc in Computer Science from the Technical University Eindhoven, The Netherlands. After a couple of years working in industry as a software developer for a large IT company in The Netherlands, Ronald decided to go back to academia. He obtained a Ph.D., again in Computer Science, from the University of Leeds, UK. Ronald’s research interests have revolved around making semantic web technologies more usable for end users, which has required research into the areas of ontology authoring and reasoning, natural language interfaces, dialogue systems, intelligent user interfaces and user modelling. Andres Garcia-Silva is a senior research scientist at Expert System, where he works on a variety of fields related to knowledge management and artificial intelligence including semantic technologies, natural language processing, information extraction and retrieval, and machine learning. Andrés holds a Ph.D. and a Master degree in Artificial Intelligence from Universidad Politécnica de Madrid. He has worked as a visiting researcher at the University of Southampton, the Free University of Berlin, and the University of Southern California. Andrés regularly publishes and reviews papers for conferences and workshops in the semantic web research community.


Best Sellers


Product Details
  • ISBN-13: 9783030448295
  • Publisher: Springer Nature Switzerland AG
  • Publisher Imprint: Springer Nature Switzerland AG
  • Height: 235 mm
  • No of Pages: 268
  • Returnable: Y
  • Width: 155 mm
  • ISBN-10: 3030448290
  • Publisher Date: 17 Jun 2020
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Sub Title: Combining Neural Models and Knowledge Graphs for NLP


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP
Springer Nature Switzerland AG -
A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    New Arrivals


    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!