Finite-State Text Processing.

The rise in popularity of neural network methods in computational linguistics has led to a richness of valuable books on the topic. On the other hand, there is arguably a shortage of recent materials on foundational computational linguistics methods like finite-state technologies. This is unfortunate, as finite-state approaches not only still find much use in applications for speech and text processing (the core focus of this book), but seem also to be valuable in interpreting and improving neural methods. Moreover, the study of finite-state machines (and their corresponding formal languages) is still proving insightful in theoretical linguistics analyses. In this sense, this book by Gorman and Sproat is a welcome, refreshing contribution aimed at a variety of readers. The book is organized in eight main chapters, and can be conceptually divided into two parts. The first half of the book serves as an introduction to core concepts in formal language and automata theory (Chapter 1), the basic design principles of the Python library used through the book (Chapter 2), and a variety of finite-state algorithms (Chapters 3 and 4). The rest of the book exemplifies the formal notions of the previous chapters with practical applications of finite-state technologies to linguistic problems like morphophonological analysis and text normalization (Chapter 5, 6, 7). The last chapter (Chapter 8) presents an interesting discussion of future steps from a variety of perspectives, connecting finite-state methods to current trends in the field. In what follows, I discuss the contents of each chapter in more detail. Chapter 1 provides an accessible introduction to formal languages and automata theory, starting with a concise but helpful historical review of the development of finite-state technologies and their ties to linguistics. The chapter balances intuitive explanations of technical concepts without sacrificing formal rigor, in particular in the presentation of finite-state automata/transducers and their formal definitions. Weighted finite-state automata play a prominent role in the book and the authors introduce them algebraically through the notion of semiring (Pin 1997). This is a welcome choice that makes the book somewhat unique among introductory/application-oriented materials on these topics. Unsurprisingly, this section is the densest part of the chapter and it could have benefitted from an explanation of the intuition behind the connection between wellformedness of a string, recognition by an automaton, and paths over semirings— especially considering how relevant such concepts become when the book transitions