Introduction and Word-Level Analysis
Dhanalekshmi Prasad Yedurkar, Ganesh Rajaram Pathak, Manisha Galphade, Thompson Stephan · 2025
Natural Language Processing (NLP) is a critical domain of Artificial Intelligence that enables machines to understand, interpret, and generate human language. This chapter explores the history of NLP, the structure of a generic NLP system, and the inherent challenges, including ambiguity in language processing. Various linguistic components, such as words, corpora, and phases of NLP, including morphological analysis, syntax analysis, semantic analysis, discourse integration, and pragmatic analysis, are discussed. Essential text preprocessing techniques, including stemming, lemmatization, normalization, and tokenization, are covered to highlight their role in language modeling. Furthermore, foundational methods like the Bag-of-Words model, regular expressions (RE), finite-state automata (FSA), finite-state transducers (FST), and n-gram language models are analyzed for their contribution to text representation and language understanding. This study provides a comprehensive overview of NLP techniques, addressing key challenges and methodologies essential for building intelligent language-processing systems.