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What is natural language processing?

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The aim of artificial intelligence (A.I.) is to allow machines to receive information and interpret it in the same intelligent way that humans do. Natural language processing (NLP) is a popular sub-category of A.I.

NLP is the study of how machines analyze natural languages and produce meaningful information about the text.

In other words, NLP is used to teach a machine how to read and understand human languages. Trained machines ​can extract the relationships between words, identify the entities in a sentence (i.e., entity-recognition), and so much more!

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Choosing the data set

For a machine to perform an NLP task, you must first train it on a dataset relevant to that particular task. This dataset will usually be a large corpus of text, like Wikipedia.

A corpus of Wikipedia and newspaper articles could be useful for named-entity recognition tasks as they contain meaningful information about different entities.

Choosing an appropriate dataset is very important because low-quality training data will result in an inaccurate model.

Training the model

Once the dataset is obtained and refined, it is time to use it to train the model.

There are various techniques used to train a model on a given corpus. Data scientists are continuously researching new ways to create the most accurate model.

One popular approach is to use word vectorization, where words are converted into vectors. The vectors of two similar words will be closer together than the vectors of two non-similar words.

The context in which the word is used can also give more information about its meaning.

Applications of natural language processing

NLP has numerous applications in the modern world. Some of them are:

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Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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