Full Description
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.
Contents
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
