The review of *Foundations of Statistical Natural Language Processing* (FSNL) by Lillian Lee highlights the book's comprehensive scope and its value as a one-stop reference for researchers, educators, and newcomers to the field. The book covers foundational concepts in probability, information theory, linguistic concepts, and empirical methods, along with traditional tools like Markov models, probabilistic grammars, and classification techniques. It also delves into specific problems such as lexicon acquisition, word sense disambiguation, parsing, machine translation, and information retrieval. The review notes that the book's structure, which presents fundamentals on a need-to-know basis, allows for a gentle introduction to the field and early project development. However, the lack of a unified underlying theory and inconsistent mathematical detail can obscure important connections and limit the book's ability to provide a cohesive overview. Despite these limitations, the reviewer commends the authors for their efforts and acknowledges the current need for a comprehensive reference in statistical NLP.The review of *Foundations of Statistical Natural Language Processing* (FSNL) by Lillian Lee highlights the book's comprehensive scope and its value as a one-stop reference for researchers, educators, and newcomers to the field. The book covers foundational concepts in probability, information theory, linguistic concepts, and empirical methods, along with traditional tools like Markov models, probabilistic grammars, and classification techniques. It also delves into specific problems such as lexicon acquisition, word sense disambiguation, parsing, machine translation, and information retrieval. The review notes that the book's structure, which presents fundamentals on a need-to-know basis, allows for a gentle introduction to the field and early project development. However, the lack of a unified underlying theory and inconsistent mathematical detail can obscure important connections and limit the book's ability to provide a cohesive overview. Despite these limitations, the reviewer commends the authors for their efforts and acknowledges the current need for a comprehensive reference in statistical NLP.