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 series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information and data-processing systems of all kinds, no matter whether human, (other) animal or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and phi­ losophical psychology through issues in cognitive psychology and socio­ biology (concerning the mental capabilities of other species) to ideas related to artificial intelligence and computer science. While primary emphasis will be placed upon theoretical, conceptual and epistemologi­ cal aspects of these problems and domains, empirical, experimental and methodological studies will also appear from time to time. Among the most challenging and difficult projects within the scope of artificial intelligence is the development and implementation of com­ puter programs suitable for processing natural language. Our purpose in compiling the present volume has been to contribute to the foundations of this enterprise by bringing together classic papers devoted to crucial problems involved in understanding natural language, which range from issues of formal syntax and logical form to those of possible-worlds and situation semantics. The book begins with a comprehensive introduc­ tion composed by Jack Kulas, the senior editor of this work, which pro­ vides a systematic orientation to this complex field, and ends with a selected bibliography intended to promote further research. If our efforts assist others in dealing with these problems, they will have been worthwhile. J. H. F.

Parsing Efficiency is crucial when building practical natural language systems. 'Ibis is especially the case for interactive systems such as natural language database access, interfaces to expert systems and interactive machine translation. Despite its importance, parsing efficiency has received little attention in the area of natural language processing. In the areas of compiler design and theoretical computer science, on the other hand, parsing algorithms 3 have been evaluated primarily in terms of the theoretical worst case analysis (e.g. lXn», and very few practical comparisons have been made. This book introduces a context-free parsing algorithm that parses natural language more efficiently than any other existing parsing algorithms in practice.

This book examines the latest research achievements of these technologies and provides a reference for researchers, engineers, students, and other interested readers. It helps readers understand the opportunities and challenges faced by deep learning and reinforcement learning and how to address them, thus improving the research and application capabilities of these technologies in related fields.

An intuitive introduction to processing natural language data with TensorFlow-Keras deep learning models.

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.

This book examines the problem of linearization from a new perspective: that of the linearization of affixes. The author’s driving proposition is that affixation provides a means of satisfying the universal requirement to linearize linguistic outputs. This proposition is tested using original data from Nuu-chah-nulth ("Nootka"; Wakashan family), an endangered Amerindian language that is remarkable for its complex morphology.

A group of authors containing both leading authorities and young researchers addresses a number of issues of contrastiveness, polarity items and exhaustivity, quantificational expressions and the implicatures they generate, and the interaction between semantic operators and speech acts. The 19 contributions provide insights on the interplay between semantics and pragmatics.

Sentiment Analysis | Speech to text | Word Cloud | Spam Detection | NLP Concepts | Code Walkthrough

Natural Language Processing : Basics to Advanced with pratical implementation using Python, TensorFlow, spaCy, gensim

A Beginner Friendly Introduction to Deep Learning and Artificial Intelligence with Python: Word Embedding from Scratch

Sentence Embeddings, Generative Transformer Models