Natural Language Computing
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Natural Language Computing
This book's main goal is to show readers how to use the linguistic theory of Noam Chomsky, called Universal Grammar, to represent English, French, and German on a computer using the Prolog computer language. In so doing, it presents a follow-the-dots approach to natural language processing, linguistic theory, artificial intelligence, and expert systems. The basic idea is to introduce meaningful answers to significant problems involved in representing human language data on a computer.
Artificial Intelligence and Natural Language Processing
Artificial Intelligence and Natural Language Processing is designed for students of computer science and linguistics at graduate and post-graduate levels, who have an interest in Natural Language Processing (NLP). This book balances the theoretical aspects of computer science and linguistics with their applications in NLP, keeping in mind the background of the students from either direction. Theories of linguistics such as phonology, morphology, syntax, semantics and lexicography are covered in the first section of the book. In the second, focus is given to theoretical aspects of computer science including AI, concepts of probability theory and approaches of machine learning. These two parts will provide students with the foundations of the field. The third part discusses the applications/tasks of NLP covering the areas of machine translation (MT) and grammar formalism in an Indian Language scenario, as well as speech processing.
Introduction to Natural Language Processing
A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.