The Natural Language for Artificial Intelligence presents the biological and logical structure typical of human language in its dynamic mediating process between reality and the human mind. The book explains linguistic functioning in the dynamic process of human cognition when forming meaning. After that, an approach to artificial intelligence (AI) is outlined, which works with a more restricted concept of natural language that leads to flaws and ambiguities. Subsequently, the characteristics of natural language and patterns of how it behaves in different branches of science are revealed to indicate ways to improve the development of AI in specific fields of science. A brief description of the universal structure of language is also presented as an algorithmic model to be followed in the development of AI. Since AI aims to imitate the process of the human mind, the book shows how the cross-fertilization between natural language and AI should be done using the logical-axiomatic structure of natural language adjusted to the logical-mathematical processes of the machine. - Presents a comprehensive approach to natural language and its inherent and complex dynamics - Develops language content as the next frontier, identifying the universal structure of language as a common structure that appears in both AI and cognitive computing - Explains the standard structure present in cognition and AI, making them interchangeable - Offers examples of the application of the universal language model in image analysis and conventional language

​This book constitutes the refereed proceedings of the 9th Conference on Artificial Intelligence and Natural Language, AINL 2020, held in Helsinki, Finland, in October 2020. The 11 revised full papers and 3 short papers were carefully reviewed and selected from 36 submissions. Additionally, the volume presents 1 shared task paper. The volume presents recent research in areas of of text mining, speech technologies, dialogue systems, information retrieval, machine learning, articial intelligence, and robotics.

This book constitutes the refereed proceedings of the 8th Conference on Artificial Intelligence and Natural Language, AINL 2019, held in Tartu, Estonia, in November 2019. The 10 revised full papers and 2 short papers were carefully reviewed and selected from 34 submissions. The papers are organized according to the following topics: ​data acquisition and annotation; human-computer interaction; statistical natural language processing; neural language models.

The book covers theoretical work, approaches, applications, and techniques for computational models of information, language, and reasoning. Computational and technological developments that incorporate natural language are proliferating. Adequate coverage of natural language processing in artificial intelligence encounters problems on developments of specialized computational approaches and algorithms. Many difficulties are due to ambiguities in natural language and dependency of interpretations on contexts and agents. Classical approaches proceed with relevant updates, and new developments emerge in theories of formal and natural languages, computational models of information and reasoning, and related computerized applications. Its focus is on computational processing of human language and relevant medium languages, which can be theoretically formal, or for programming and specification of computational systems. The goal is to promote intelligent natural language processing, along with models of computation, language, reasoning, and other cognitive processes.

We met because we both share the same views of language. Language is a living organism, produced by neural mechanisms relating in large numbers as a society. Language exists between minds, as a way of communicating between them, not as an autonomous process. The logical 'rules' seem to us an epiphe nomena ·of the neural mechanism, rather than an essential component in language. This view of language has been advocated by an increasing number of workers, as the view that language is simply a collection of logical rules has had less and less success. People like Yorick Wilks have been able to show in paper after paper that almost any rule which can be devised can be shown to have exceptions. The meaning does not lie in the rules. David Powers is a teacher of computer science. Christopher Turk, like many workers who have come into the field of AI (Artificial Intelligence) was originally trained in literature. He moved into linguistics, and then into computational linguistics. In 1983 he took a sabbatical in Roger Shank's AI project in the Computer Science Department at Yale University. Like an earlier visitor to the project, John Searle from California, Christopher Turk was increasingly uneasy at the view of language which was used at Yale.

This volume focuses on natural language processing, artificial intelligence, and allied areas. Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This book discusses theoretical work and advanced applications, approaches, and techniques for computational models of information and how it is presented by language (artificial, human, or natural) in other ways. It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It explores the difficult problems and challenges related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Key features: Addresses the functional frameworks and workflow that are trending in NLP and AI Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP.

​This book constitutes the refereed proceedings of the 11th Conference on Artificial Intelligence and Natural Language, AINL 2022, held in St. Petersburg, Russia, in April 2022. The 8 revised full papers and 1 short paper were carefully reviewed and selected from 20 submissions. The volume presents recent research in areas of of text mining, speech technologies, dialogue systems, information retrieval, machine learning, articial intelligence, and robotics.

This handbook provides a comprehensive understanding of computational linguistics, focusing on the integration of deep learning in natural language processing (NLP). 18 edited chapters cover the state-of-the-art theoretical and experimental research on NLP, offering insights into advanced models and recent applications. Highlights: - Foundations of NLP: Provides an in-depth study of natural language processing, including basics, challenges, and applications. - Advanced NLP Techniques: Explores recent advancements in text summarization, machine translation, and deep learning applications in NLP. - Practical Applications: Demonstrates use cases on text identification from hazy images, speech-to-sign language translation, and word sense disambiguation using deep learning. - Future Directions: Includes discussions on the future of NLP, including transfer learning, beyond syntax and semantics, and emerging challenges. Key Features: - Comprehensive coverage of NLP and deep learning integration. - Practical insights into real-world applications - Detailed exploration of recent research and advancements through 16 easy to read chapters - References and notes on experimental methods used for advanced readers Ideal for researchers, students, and professionals, this book offers a thorough understanding of computational linguistics by equipping readers with the knowledge to understand how computational techniques are applied to understand text, language and speech.

This book covers theoretical work, applications, approaches, and techniques for computational models of information and its presentation by language (artificial, human, or natural in other ways). Computational and technological developments that incorporate natural language are proliferating. Adequate coverage encounters difficult problems related to ambiguities and dependency on context and agents (humans or computational systems). The goal is to promote computational systems of intelligent natural language processing and related models of computation, language, thought, mental states, reasoning, and other cognitive processes.

Dr.Ravi Kumar Saidala, Associate Professor, Department of CSE – Data Science, CMR University, Bangalore, Karnataka, India. Mr.Satyanarayanareddy Marri, Assistant Professor, Department of Artificial Intelligence, Anurag University, Hyderabad, Telangana, India. Dr.D.Usha Rani, Associate Professor, Department of Computer Science and Applications, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India. Prof.U.Ananthanagu, Assistant Professor, Department of CSE, Alliance University, Bangalore, Karnataka, India.