Survey of Application of Automata Theory in Natural Language Processing

With a focus on finite-state automata (FSA), this overview explores the various uses of automata in natural language processing (NLP). Automata are used in a variety of natural language processing (NLP) applications, such as pattern matching, recognition, processing audio and handwritten text, encryption, data compression, and operating system analysis. An extensive review of theoretical automata models, including pushdown automata, Turing machines, and FSA, is provided in the abstract. With a focus on tokenization, parsing, and syntactic analysis, the paper investigates how automata might be integrated with contemporary deep learning to improve the effectiveness of natural language processing. Among the difficulties discussed in the study is the scalability of automatadriven models in big language datasets. A nuanced view of the dynamic environment of automata in NLP is offered by this synthesis.