Natural Language Processing is the branch of Artificial
Intelligence involving language, be it in spoken or written
modality. Teaching Natural Language Processing is difficult due to
its inherent connections with other disciplines, such as
Linguistics, Cognitive Science, Knowledge Representation, Machine
Learning, Data Science, and its latest avatar: Deep Learning. Most
introductory NLP books favor one of these disciplines at the
expense of others. This book aims to consider three points of view
corresponding to three different disciplines, while granting equal
importance to each of them. Based on a course on Natural
Language Processing taught by the author at IMT Atlantique (a
grande école d'ingénieurs based in Brest, France) for more than a
decade, this book provides a thorough introduction into the topic,
based on three threads: the fundamental notions of Linguistics,
symbolic Artificial Intelligence methods (based on knowledge
representation languages) and statistical methods (involving both
legacy machine learning and deep learning tools). Complementary to
this introductory text is teaching material such as exercises with
solutions, labs with programming code (in Python), exams (with
answers and discussion of specific difficulties) and general
questions assigned to students as homework, including answers,
discussion, and links to related general audience books or
scientific publications. This material can serve for classes
given to undergraduate students, or for professionals in computer
science or linguistics wanting to acquire knowledge in the field.
As everything is explained, the book is suitable, and warmly
recommended, for self-study.
General
Imprint: |
Springer International Publishing AG
|
Country of origin: |
Switzerland |
Release date: |
February 2024 |
Authors: |
Yannis Haralambous
|
Dimensions: |
235 x 155mm (L x W) |
Edition: |
1st ed. 2023 |
ISBN-13: |
978-3-03-127225-7 |
Categories: |
Books
|
LSN: |
3-03-127225-0 |
Barcode: |
9783031272257 |
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