Study of Finite State Machines as Language Recognizer - Sarita Tiwari; Kavita Tiwari; Kirti Verma; Ayush Wardhan Sahu (International Journal of Data Structure Studies, 2023)
Finite State Machines (FSMs) play a fundamental role in computer science and linguistics as language recognizers. This study presents an exploration of the principles and applications of FSMs as efficient tools for recognizing formal languages. The study delves into the theoretical foundations of FSMs and their practical implementation in various language recognition tasks. The fundamental ideas of FSMs, including as states, transitions, and input symbols, are introduced in this study. A clear understanding of these components is essential in constructing FSMs capable of recognizing specific languages. Additionally, the fundamental types of FSMs, namely deterministic and nondeterministic, are discussed, with a focus on their unique properties and computational differences. It examines the theoretical power of FSMs in classifying languages. We analyse the Chomsky hierarchy, which categorizes languages into four classes based on their generative power: regular, context-free, context-sensitive, and recursively enumerable. We illustrate how FSMs effectively identify regular languages, the simplest class in the hierarchy, and the limits of their expressive capacity for more complex languages. The study investigates the application of FSMs in various language recognition scenarios. We demonstrate how FSMs can be employed in lexical analysis for programming languages, enabling efficient tokenization and syntax parsing. In order to demonstrate their adaptability in many linguistic situations, we also study their function in natural language processing, such as part-of-speech tagging and named entity recognition. We examine the difficulties and restrictions faced by FSMs when used as language recognizers. While FSMs are powerful tools for regular languages, they face constraints when dealing with context-dependent phenomena present in more complex languages. We discuss possible extensions and hybrid approaches to overcome these limitations. This study presents case studies and practical implementations of FSMs in real-world language recognition systems. We highlight success stories in automating various language-related tasks, underscoring the effectiveness and efficiency of FSM-based approaches.
Keywords: Finite State Machines (FSMs); Mealy State Machine; Moore state machine; recursively enumerable; regular languages