The Origins Of Nlp

The Origins of NLP brings together the recollections and thoughts of some of the main protagonists from the very early days of NLP. In 1971 Richard Bandler and Frank Pucelik were students at Kresege College at the University of California Santa Cruz. They had a strong mutual interest in Gestalt Therapy, Frank because of his traumatic time in Vietnam and because he had been working with some disaffected and drug-addicted kids, and Richard because he had been working with Science and Behavior Books on transcribing and editing Fritz Perls' seminal work, The Gestalt Approach and Eyewitness to Therapy. They started a local Gestalt group and ran 2-3 sessions a week collaborating and experimenting with the language of therapy. They started achieving some brilliant results but were having problems transferring their skills to others and so Richard invited one of their college professors, John Grinder, to observe what they were doing in order that he would, hopefully, be able to deconstruct what they were doing that was so effective. John was a professor of Linguistics and was instantly impressed with the work that they were doing. He was able to add more structure and in due course the three of them formalised what is now known as the Meta Model. NLP, or Meta as it was known then, was born.

A collection of activities developed and used with teenagers all over the country that are short, easy to follow and engaging. They can be used as one off activities to spice up a session or can be put together to form one hour lessons or even whole day events. There are suggested combinations of activities to suit different topi such as PSHE, Successful Revision/Learning, SEAL.

NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production

Here is a practical and clearly written guide to the use of Neuro-Linguistic Programming in the treatment of alcoholism and other addictions. This comprehensive volume illustrates how the focus of Neuro-Linguistic Programming (NLP) on the individual and the family increases the effectiveness of counseling by targeting the uniqueness of each individual and his or her family system. Professionals will also learn how NLP facilitates effective interventions and helps alcoholics create internal coping skills to begin and maintain the recovery process. Neuro-Linguistic Programming in Alcoholism Treatment is a wealth of innovative, state-of-the-art information on the history of NLP, basic NLP assumptions, concepts for establishing rapport with clients, and essential family participation in NLP. Each chapter directs itself either to a specific NLP technique, featuring clear case demonstrations and a step-by-step outline for applying the technique to the development of the counseling process in working with addicted systems, or to a particular group affected by addiction, such as alcoholics, children of alcoholics, or the addicted family as a unit. Clinicians focus on alcoholism and other addictions as treatable conditions, applying specific strategies and techniques--initially developed in Neuro-Linguistic Programming and Hypnotherapy--adapted to meet the demands of alcoholism and addiction treatment specialists. This important volume allows for a substantial increase in the repertoire of treatment choices available to professionals and enables clinicians to individualize treatment. An extensive bibliography is included to further assist readers in gaining additional skills in the treatment of alcoholics and other addicts.

If you want to build an enterprise-quality application that uses natural language text but aren’t sure where to begin or what tools to use, this practical guide will help get you started. Alex Thomas, principal data scientist at Wisecube, shows software engineers and data scientists how to build scalable natural language processing (NLP) applications using deep learning and the Apache Spark NLP library. Through concrete examples, practical and theoretical explanations, and hands-on exercises for using NLP on the Spark processing framework, this book teaches you everything from basic linguistics and writing systems to sentiment analysis and search engines. You’ll also explore special concerns for developing text-based applications, such as performance. In four sections, you’ll learn NLP basics and building blocks before diving into application and system building: Basics: Understand the fundamentals of natural language processing, NLP on Apache Stark, and deep learning Building blocks: Learn techniques for building NLP applications—including tokenization, sentence segmentation, and named-entity recognition—and discover how and why they work Applications: Explore the design, development, and experimentation process for building your own NLP applications Building NLP systems: Consider options for productionizing and deploying NLP models, including which human languages to support