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Natural Language Processing (NLP) is a division of artificial intelligence that focuses on how to program computers to process natural language, meaning human speech. It combines many disciplines: computer science, information engineering, and even linguistics come together to analyse interactions between humans and computers. The goal is to translate human languages into valuable data.

NLP frequently relies on machine learning. It is generally used in tools such as chatbots, search recommendations, and personalised text-based marketing. It can also help perform tasks such as translation and speech recognition, among others. NLP engineers work on apps or tools that rely on NLP.

In more detail

NLP engineers create software that understands human language. This is not a simple task due to the complexity of our speech, which also varies from language to language.

NLP can be used for different purposes, for example: summarising texts; generating keyword tags from content (to discover the topic of a body of text); identifying the sentiment of a text. Grammarly is one great example of natural language processing, as it corrects users’ writing and identifies the tone of the text, among other functions. In general, NLP engineers design and develop applications with the kind of functionalities outlined above.

They must be knowledgeable about the different tools and algorithms to implement for NLP tasks, and subject their models to tests. They are responsible for ensuring the optimal performance of the applications.

In order to work as an NLP engineer, it is necessary to have a strong background in machine learning and a good understanding of math. An advanced degree in Computer Science, Machine Learning, Computational Linguistics, or a related field is necessary for this kind of position.

Some knowledge of linguistics is hugely beneficial for NLP. These specialists must, however, be capable of designing software architecture. Some commonly used programming languages are R, Python, and Java. Some experience with ML (Machine Learning) frameworks, such as Pytorch, is a plus.

Finally, NLP engineers must be great problem-solvers and effective communicators.

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