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Preface Chapter 1£ºIntroduction Understanding natural language processing Understanding basic applications Understanding advanced applications Advantages of togetherness¡ªN LP and Python Environment setup for NLTK Tips for readers Summary Chapter 2£ºPractical Understanding of a Corpus and Datase What is a corpus Why do we need a corpus UnderStanding corpus analysis Exercise Understanding types of data attributes Categorical or qualitative data attributes Numeric or quantitative data attributes Exploring different file formats for corpora Resources for accessing free corpora Preparing a dataset for NLP applications Selecting data Preprocessing the dataset Formatting Cleaning Sampling Transforming data Web scraping Summary Chapter 3£ºUnderstanding the Structure of a Sentences Understanding components of NLP Natural language understanding Natural language generation Differences between NLU and NLG Branches nf NLP Defining context-free grammar Exercise Morphological analysis What is morphology What are morphemes What is a stem What is morphological analysis What iS a word Classification of morphemes Free morphemes Bound morphemes Derivational morphemes Inflectional morphemes What is the difference between a stem and a root Exercise Lexical analysis Whal is a token What are part of speech tags Process of deriving tokens Difference between stemming and lemmatization Applications Syntactic analysis What is syntactic analysis Semantic analysis What is semantic analysis Lexical semantics Hyponymy and hyponyms Homonymy Polysemy What is the difference between polysemy and homonymy Application of semantic analysis Handling ambiguity Lexical ambiguity Syntactic ambiguity Approach to handle syntactic ambiguity Semantic ambiguity Pragmatic ambiguity Discourse integration Applications Pragmatic analysis Summary Chapter 4: PreproceSSing Chapter 5: Feature Engineering and NLP Alclorithms Chapter 6£ºAdvanced Feature Engineering and NLP Algorithms Chapter 7: Rule-Based System for NLP Chapter 8: Machine Learning for NLP Problems Chapter 9: Deep Learnincl for NLU and NLG Problems Chapter 10: Advanced Tools Chapter 11 : How to Improve Your NLP Skills Chapter 12: Installation Guide Index