Modern NLP practitioners in Brisbane are thinking about NLP research and how to put them into practice. As we all know, Neuro-linguistic programming (NLP) is a pseudoscientific approach to communication, personal development, and psychotherapy that believes that there is a connection between neurological processes, language, and behavioral patterns learned through experience (programming). Practitioners believe that all of these patterns can be changed to achieve certain goals in life.
Pre-modern NLP is similar to old school stuff in the ’80s, in NLP textbook. It’s all about a lot of linguistics like focusing on what are the rules of language, diagramming, and grammar. Old school NLP focused on formal grammars, sentence constructions, and syntax.
Modern NLP Practitioners in Brisbane
Due to the challenges of old school NLP, practitioners have developed modern NLP. They asked what are people who are researching and doing NLP in this century, thinking about and focusing on?
One theme is neural nets and low-dimensional representation. Many data analysis tasks deal with data presented in high-dimensional spaces. The first key step in solutions of these tasks is to transform the original high-dimensional data into their lower-dimensional representations so that as much information as possible is preserved about the original data required for the considered task.
A second theme is putting things in context. A lot of the real innovation that’s going on is around taking words. The third theme is big data like the ones we get from the internet and social media. A fourth theme is the use of computing power. We use the cloud now for learning and this is a contributing factor in NLP. Then finally, the fifth theme is the transfer of learning. All of the themes above lead to the transfer of learning which modern NLP practitioners uphold to do.
Modern NLP practitioners Brisbane try to discover new ways that are fundamental in the building blocks of neural network procedure and how they are utilized to tackle problems in modern natural language processing.
They dig more on language vector representations, text classification, named entity recognition, and sequence to sequence modeling approaches. They emphasize the shape of these types of problems from the perspective of deep learning structure or natural language processing called deep learning.
Modern NLP practitioners who deal with deep learning try to build up an intuition from the ground up using a highly visual approach to describe neural networks and the description of problems that can be solved with modern NLP techniques.