Artificial Intelligence for Natural Language Processing

1. Auflage 2025
ISBN: 978-1-032-54530-1
Verlag: Taylor & Francis Ltd

Buch, Englisch, 104 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 453 g

Artificial Intelligence for Natural Language Processing offers a comprehensive exploration of how advanced computational methods are transforming the way machines understand human language. This book delves into the core principles of Natural Language Processing (NLP) through an engaging progression—from fundamental word-level analysis to complex discourse and pragmatic analysis—integrating linguistic theory with cutting-edge AI methodologies. It provides a robust framework for both the theoretical underpinnings and practical applications of NLP, ensuring that readers gain a clear understanding of how computers can effectively process and interpret human language.

What sets this book apart is its methodical structure that guides the reader through each level of language analysis, building upon earlier chapters to culminate in a deep integration of artificial intelligence within NLP systems. The detailed explanations and examples are designed to bridge the gap between abstract theory and real-world application, making it an invaluable resource for anyone looking to grasp the nuances of language processing.

Key features:

· A step-by-step progression from word-level analysis to syntactic, semantic, and pragmatic processing.

· In-depth discussions on word sense disambiguation with illustrative examples.

· An exploration of discourse integration and contextual meaning essential for modern NLP models.

· Comprehensive coverage of AI applications in NLP, highlighting state-of-the-art computational techniques.

· Clear, accessible explanations suitable for both beginners and advanced practitioners.

This book is ideal for graduate students, researchers, and professionals in computer science, linguistics, and artificial intelligence. Whether you are a seasoned researcher looking to deepen your understanding or a newcomer eager to explore the field, Artificial Intelligence for Natural Language Processing serves as both an essential academic resource and a practical guide for navigating the evolving landscape of language technology.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Yedurkar, Dhanalekshmi Prasad

Pathak, Ganesh R.

Galphade, Manisha

Stephan, Thompson

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Inhaltsverzeichnis

Preface Author Biographies CHAPTER 1: INTRODUCTION AND WORD LEVEL ANALYSIS 1. Introduction and Word Level Analysis 1.1 History of NLP 1.2 Generic NLP System 1.3 Ambiguity and Challenges 1.4  Words 1.5 Corpora 1.6 Phases of NLP 1.6.1 Morphological / Lexical Analysis 1.6.2 Syntax Analysis or Parsing 1.6.3 Semantic Analysis 1.6.4 Discourse Integration 1.6.5 Pragmatic Analysis 1.7 Basic Concepts of Text Preprocessing 1.7.1 Stemming 1.7.2 Lemmatization 1.7.3 Normalization 1.7.4 Tokenization 1.7.5 Bag of Words 1.7.6 Regular Expression (RE) 1.7.7 Finite State Automaton (FSA) 1.7.8 Finite State Transducer (FST) 1.7.9 N Grams Language Model CHAPTER 2: SYNTACTIC ANALYSIS 2.1 Parts Of Speech ( POS ) Tagging 2.1.1 Rule Based Tagging 2.1.2 Stochastic POS Tagging 2.2 Stop Words 2.3 Sequence Labeling 2.3.1 The Hidden Markov Model (HMM) 2.3.2 The Conditional Random Field ( CRF ) 2.4 Context Free Grammar ( CFG ) 2.5 Parsing 2.5.1 Earley Parsing 2.5.2 Cky Parsing 2.6 Probabilistic Context Free Grammar ( PCFG ) 2.7 Term Frequency And Inverse Document Frequency ( TF-IDF ) 2.8 Information Extraction 2.9 Relation Extraction CHAPTER 3: SEMANTIC ANALYSIS 3.1 Semantic Grammar 3.2 Lexical Semantics 3.3 Lexemes 3.4 Word Senses 3.4.1 Hyponymy 3.4.2 Homonymy 3.4.3 Polysemy 3.4.4 Synonymy 3.4.5 Antonymy 3.5 Wordnet 3.6 Word Similarity 3.7 Word Sense Disambiguation (WSD) 3.7.1 Dictionary Based Approach of WSD 3.8 Information Retrieval CHAPTER 4: DISCOURSE AND PRAGMATIC ANALYSIS 4.1 Important Terms 4.2 Ethnography of Speaking 4.3 Implicature 4.4 Cooperative Principle 4.5 Schema-Script 4.6 Conversational Analysis 4.7 Deciphering Meaning and Coherence Of Text Data 4.7.1 Endophora 4.7.2 Exophora 4.8 Discourse Context and its Types 4.9 Speech Acts 4.9.1 Direct Speech Act 4.9.2 Indirect Speech Act 4.10 Deixis and Deictic Expressions 4.11 Positive and Negative Face in Pragmatics 4.11.1 Positive Face 4.11.2 Negative Face 4.12 Pragmatic Markers and Functions 4.12.1 Functions of Pragmatic Markers CHAPTER 5: ARTIFICIAL INTELLIGENCE IN NLP 5.1 Machine Learning 5.1.1 Supervised Machine Learning 5.1.2 Unsupervised Machine Learning 5.2 Machine Learning on Natural Language Sentences 5.3 Hybrid Machine Learning Systems in NLP 5.4 Introduction to Deep Learning in NLP 5.5 Applications of NLP 5.5.1 Sentiment Analysis 5.5.2 Prediction of Next Word Index

Über Autor(innen)

Dhanalekshmi Prasad Yedurkar is a postdoctoral researcher at the University of Augsburg, Germany. She is also affiliated to MIT Art, Design & Technology University, Pune, India, as an associate professor in School of Computing. She earned her doctorate on the application of digital signal processing and artificial intelligence in anomaly detection of EEG signal. Her current research interests include Natural language processing, tool condition monitoring for CNC processes, diagnosis and prognosis of gear monitoring, biomedical signal processing, machine learning, Internet of Things and Image processing.

Ganesh R. Pathak is an academic and a researcher with over 26 years of experience bridging academia and industry. He is currently a Professor and Head of the Department of Computer Science and Engineering at School of Computing, MIT ADT University, Pune. He earned his Ph.D. in Computer Science and Engineering, with his research focused on developing a security framework for wireless sensor networks. He has published 34 research papers published in reputed peer-reviewed journals and conference proceedings, many of which are indexed in Scopus and SCI. His research and teaching interests focus on artificial intelligence, big data analytics, and cognitive modelling. As a mentor, he guides doctoral candidates in artificial intelligence, data science, cloud computing and security.

Beyond his research, he is an active contributor to the academic and professional community. He serves on various boards of studies, contributes as a reviewer and session chair at national and international conferences. He also promotes automation and e-governance in educational processes within the university. His efforts extend to skill development having organised numerous workshops, seminars, and faculty development programs, which strengthen his role as an admonitor in academic.Through his scholarly achievements, project leadership, and engagement in the academic community, he continues to make contributions to both within university, various other institutions in advancing technology education and research.

Manisha Galphade is an experienced academic professional currently working at School of Computing, MIT Art, Design and Technology University, Pune. With a teaching career spanning 16 years, she has contributed significantly to the field of education. She teaches subjects including Machine Learning, Database Management System, Theory of Computation, Data Mining, and many more. Throughout her career, she has authored 4 conference papers, 5 journal papers, and 3 book chapters, showcasing her dedication to research and academic development. She is also pursuing a PhD from Veermata Jijabai Technological Institute, Mumbai, furthering her academic expertise. Her research area is Artificial Intelligence, with research interests in Time Series Analysis, Image Processing, and Signal Processing. Passionate about fostering innovation and critical thinking, she strives to inspire students to reach their full potential. Her extensive experience and research contributions reflect her commitment to advancing knowledge and fostering growth in her field.

Thompson Stephan earned his PhD from Pondicherry University, India, in 2018, has nearly seven years of academic experience, complemented by full-time research and industry expertise. He serves as an Assistant Professor at Thumbay College of Management and AI in Healthcare, Gulf Medical University, Ajman, United Arab Emirates. Recognized among Stanford/Elsevier’s Top 2% Scientists globally in both 2023 and 2024, Thompson has received prestigious accolades, including the Best Researcher Award in 2020 and the Protsahan Research Award in 2023, both from the IEEE Bangalore Section, India. His primary research focus is in artificial intelligence, with specialized expertise in advancing machine learning, data mining, and metaheuristic optimization. With more than 80 Scopus-indexed publications, including 48 in SCI-indexed journals, Thompson's work has garnered significant recognition. He actively contributes as a book editor and reviewer for esteemed international journals, with publications on leading platforms such as IEEE, Elsevier, Taylor & Francis, and Springer.