There are plenty of applications for machine learning, and one of those is natural language processing or NLP.

NLP handles things like text responses, figuring out the meaning of words within context, and holding conversations with us. It helps computers understand the human language so that we can communicate in different ways.

From chat bots to job applications to sorting your email into different folders, NLP is being used everywhere around us.

At its core, natural language processing is a blend of computer science and linguistics. Linguistics gives us the rules to use to train our machine learning models and get the results we’re looking for.

There are a lot of reasons natural language processing has become a huge part of machine learning. It helps machines detect the sentiment from a customer’s feedback, it can help sort support tickets for any projects you’re working on, and it can read and understand text consistently.

And since it operates off of a set of linguistic rules, it doesn’t have the same biases as a human would.

Since NLP is such a large area of study, there are a number of tools you can use to analyze data for your specific purposes.

There’s the rules-based approach where you set up a lot of if-then statements to handle how text is interpreted. Usually a linguist will be responsible for this task and what they produce is very easy for people to understand.

This might be good to start with, but it becomes very complex as you start working with large data sets.

Another approach is to use machine learning where you don’t need to define rules. This is great when you are trying to analyze large amounts of data quickly and accurately.

Picking the right algorithm so that the machine learning approach works is important in terms of efficiency and accuracy. There are common algorithms like Naïve Bayes and Support Vector Machines. Then there are the more specific algorithms like Google BERT.

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