國立高雄大學圖資館 |

A course in natural language processing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A course in natural language processingby Yannis Haralambous.
作者:	Haralambous, Yannis.
出版者:	Cham :Springer International Publishing :2024.
面頁冊數:	xvii, 534 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Natural language processing (Computer science)
電子資源:	https://doi.org/10.1007/978-3-031-27226-4
ISBN:	9783031272264$q(electronic bk.)

A course in natural language processing
Haralambous, Yannis.

A course in natural language processing[electronic resource] /by Yannis Haralambous. - Cham :Springer International Publishing :2024. - xvii, 534 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783031272264$q(electronic bk.)

Standard No.: 10.1007/978-3-031-27226-4doiSubjects--Topical Terms:

200539
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

A course in natural language processing
LDR:03574nmm a2200325 a 4500 001 662777
003 DE-He213
005 20240130094643.0
006 m d
007 cr nn 008maaau
008 241203s2024 sz s 0 eng d
020 $a 9783031272264$q(electronic bk.)
020 $a 9783031272257$q(paper)
024 7 $a 10.1007/978-3-031-27226-4 $2 doi
035 $a 978-3-031-27226-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.N38
072 7 $a UYQL $2 bicssc
072 7 $a COM073000 $2 bisacsh
072 7 $a UYQL $2 thema
082 0 4 $a 006.35 $2 23
090 $a QA76.9.N38 $b H254 2024
100 1 $a Haralambous, Yannis. $3 975064
245 1 2 $a A course in natural language processing $h [electronic resource] / $c by Yannis Haralambous.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xvii, 534 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Natural language processing (Computer science) $3 200539
650 1 4 $a Natural Language Processing (NLP) $3 826373
650 2 4 $a Computational Linguistics. $3 274669
650 2 4 $a Knowledge Based Systems. $3 913337
650 2 4 $a Data Science. $3 913495
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Symbolic AI. $3 944083
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-27226-4
950 $a Computer Science (SpringerNature-11645)