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A Practical Guide to Hybrid Natural Language Processing - Combining Neural Models and Knowledge Graphs for NLP (Hardcover, 1st ed. 2020)
Loot Price: R3,996
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A Practical Guide to Hybrid Natural Language Processing - Combining Neural Models and Knowledge Graphs for NLP (Hardcover, 1st ed. 2020)
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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.
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