What exactly is NLP?

Neuro-linguistic programming (NLP) is a set of tools that can greatly impact your success in life. If you're unfamiliar, NLP is the study of how people learn, communicate and change behavior patterns. It's commonly used in business to help individuals interact better with customers and coworkers. If you're wondering how these concepts apply to your life, keep reading to gain a better understanding of how you can use them.

The Definition of NLP

Neuro-linguistic programming is a psychological model of human behavior and change developed by Richard Bandler, John Grinder, and their colleagues. The field is also known as NLP, the study of the structure of subjective experience and its transformation. A practitioner of NLP is called an NLP coach.

In essence, NLP seeks to maximize human potential by developing skills for personal empowerment, peak performance, and optimal mental health.

NLP was created from a synthesis of many diverse fields, including linguistics, cybernetics, mathematics, neuroscience, and psychology. It is based on the observation that people have different ways of thinking about things, which are reflected in the language they use to describe them. This can be used to help people think more effectively or to change how they feel about something.

The term Neuro-linguistic programming (NLP) was coined by John Grinder and Richard Bandler in 1975 to refer to a model of communication and personal development they were developing at that time.

NLP claims that our minds are like computers that run software programs called filters (or programs). These filters have been installed over time as we have learnt new skills and information about life. They guide us through all our experiences and allow us to make sense of our world around us. They also influence what we think about ourselves and other people, so if you want to change something about yourself then you need to change the way your mind thinks about it first.

What Techniques Are Used In NLP?

The NLP process involves identifying the problem and then creating a series of steps to solve it. This process is called the NLP model and consists of six stages:

Observe - What do you see? What does it look like?

Define - What does it mean? How do you define this?

Extrapolate - What could happen if this were to continue?

Pre-suppose - What would happen if I change something else?

Create options - How can I use this information in different ways?

Choose an option - Which option should I choose?

The are several fundamental techniques that are applied in the NLP process:

Modeling

Modeling is when you take someone else's model or belief system and apply it to your own life. For example, if you want to become more confident, you could model some people who are confident (e.g., movie stars or athletes). You can then take their behaviors and attitudes and adapt them for yourself.

Reframing

Reframing refers to changing the way you look at things so that they appear in a different light. For example, if someone says something negative about you, instead of getting angry, you might look at the situation from another perspective — maybe that person was having a bad day, or perhaps he was just joking around with you?

Submodality Changes

This is one of the most fundamental NLP techniques. It involves making small changes in a person's perceptions or associations with an experience. For example, when someone thinks about a situation that makes them feel depressed or anxious, they may be experiencing it as a picture in their mind. You can change this so that it feels like a movie playing out instead (making it less vivid), or by changing its color from black-and-white to color (making it more vivid). The goal is to make something less intense so that people can deal with it better.

NLP in translation

It's a common misconception that NLP is only useful for social media filtering, chatbots, etc. But there are many different applications of this technology. One of these istranslation.

Translating documents into other languages is an extremely difficult and laborious process, especially when you're working with technical documents which contain jargon and specific terminology. The amount of time, effort, and money spent on translation can be massive if there are many different languages involved in the project.

Translators work hard to ensure that their translations are accurate and precise, but they don't always know what they're translating into or from. This can lead to mistakes or confusion if there are any differences between the two languages being used in the document or if one of them has a very specific meaning that needs to be preserved during translation.

In particular, NLP has been applied to translation in order to improve the quality of existing machine learning translation systems. This is achieved by using statistical models that try to predict the most likely translations based on a statistical analysis of parallel corpora (multiple sentences in different languages).

The idea behind this approach is that if we have two texts that are similar in meaning then they should also share similar sentence structures. In other words, if I want to translate "The cat ate the mouse" into French and I find two similar French sentences such as "Le chat mange le rat" and "La souris mange le chat", then my statistical model might predict that "La souris mange le chat" is a good translation for “The cat ate the mouse”!

What are the limitations of NLP?

It’s important to note that NLP is not a magic bullet that will solve all your problems. The purpose of NLP is to change your beliefs about yourself and the world around you, so that you can improve your life.

NLP cannot be used for all languages, especially those with complex grammar rules. For example, English has relatively simple grammar rules and is therefore easy for computers to understand. However, Chinese has many different characters that can be combined together to form new words and sentences.

The problem of ambiguity: NLP relies on the context in order to make sense of what we say. If we say "I saw this movie", there are two possible meanings: I saw it yesterday or last year, or sometime in the future. This problem becomes even more complex when we talk about past events because they are part of our memory and are therefore open to interpretation by our brains (a process called "reconstruction").

Conclusion

In the coming decades, we will see more and more NLP in our interactions with technology. Already, there are cases where conversations between humans and computers are entirely natural. For the most part, this type of technology is still in the early stages, but with more investment from businesses and research institutions, we might not be too far away from a world where natural language processing is a core component of how we interact with computers in our everyday lives. However, especially in the translation industry, we are still very far away from a 100% NLP translation.