A Course in Natural Language Processing / by Yannis Haralambous.

Format:

Book

Author/Creator:

Haralambous, Yannis, author.

Language:

English

Subjects (All):

Natural language processing (Computer science).

Computational linguistics.

Expert systems (Computer science).

Artificial intelligence--Data processing.

Artificial intelligence.

Machine learning.

Natural Language Processing (NLP).

Computational Linguistics.

Knowledge Based Systems.

Data Science.

Machine Learning.

Symbolic AI.

Local Subjects:

Natural Language Processing (NLP).

Computational Linguistics.

Knowledge Based Systems.

Data Science.

Machine Learning.

Symbolic AI.

Physical Description:

1 online resource (543 pages)

Edition:

1st ed. 2024.

Place of Publication:

Springer Nature 2024

Summary:

Natural Language Processing is the branch of Artificial Intelligence involving language, be it in spoken or written modality. Teaching Natural Language Processing (NLP) is difficult because of 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. Based on a course on Natural Language Processing taught by the author at IMT Atlantique for over a decade, this textbook considers three points of view corresponding to three different disciplines, while granting equal importance to each of them. As such, the book provides a thorough introduction to the topic following three main 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 and labs with hints and expected results. Complete solutions with Python code are provided for educators on the SpringerLink webpage of the book. This material can serve for classes given to undergraduate and graduate students, or for researchers, instructors, and professionals in computer science or linguistics who wish to acquire or improve their knowledge in the field. The book is suitable and warmly recommended for self-study. With a PhD in Algebraic Topology (Lille, 1990), Yannis Haralambous is a TeX aficionado and Full Professor at IMT Atlantique in Brest, France. His research interests cover Text Mining, Controlled Natural Languages, Knowledge Representation, and Grapholinguistics, topics in which he has published over 120 research or scientific popularization papers and a book on Fonts and Encodings (O’Reilly, 2007). He is in charge of IMT Atlantique’s “Data Science” Master track program, where he has been teaching the course that inspired this book.

Contents:

Preface

1. Introduction

Part I. Linguistics

2. Phonetics/Phonology

3. Graphetics/Graphemics

4. Morphemes, Words, Terms

5. Syntax

6. Semantics (and Pragmatics)

7. Controlled Natural Languages

Part II. Mathematical Tools

8. Graphs

9. Formal Languages

10. Logic

11

Ontologies and Conceptual Graphs

Part III. Data Formats

12. Unicode

13. XML, TEI, CDL

Part IV. Statistical Methods

14. Counting Words

15. Going Neural

16. Hints and Expected Results for Exercises

Acronyms

Index.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Haralambous, Yannis A Course in Natural Language Processing

ISBN:

3-031-27226-9