What is Natural Language Processing (NLP)?

You have probably heard of Natural Language Processing (NLP), which is very topical right now, although its origins go back more than seven decades. If the term does not ring a bell, you have certainly used or have some system that enables machines to interpret human language, such as translators, spell checkers, or the very fashionable voice assistants.

With this brief introduction, the capabilities and definition of NLP can already be inferred, but let us refine them to clarify the concepts. Natural Language Processing is a field of Artificial Intelligence that studies the interaction between people and machines through language. In other words, it aims to get machines to understand and reproduce the way we communicate, so that we can interact with them through language.

The boom in this technology in recent years—which, as noted earlier, dates back to the 1950s—has been driven by the growing ability to analyse vast volumes of data thanks to big data, the development of algorithms, and the highly significant evolution of artificial intelligence. The world, our day-to-day life, is increasingly digital, and our interaction with machines is growing and becoming ever more natural.

Fluency of human–machine interaction

In this process, language, as the vehicle of communication, is fundamental. Human–machine exchange can be based on written or spoken language, and the goal of both forms of expression is for machines to receive our messages, decode them, interpret them, and return the expected response in a way that we can understand without any additional prior knowledge.

The fluency in communication that language provides—as we have already noted, our natural form of interaction—brings multiple benefits at all levels, and is particularly important for facilitating and simplifying digitalisation and digital transformation processes in productive environments. Simpler communication enables faster, smoother implementations, as it minimises the need for technical knowledge and streamlines actions that require the joint involvement of workers and machines.

At this point, it is also important to make it clear that Natural Language Processing represents a paradigm shift that puts people at the centre of the process, since it is machines that adapt to our way of communicating and not the other way around, as had been the case until now.

Applications of Natural Language Processing

There are many applications of Natural Language Processing, and they are becoming increasingly widespread. Let us look at a few:

Text-based interaction: we can interact with computers through written text. Once the human sender issues the message, the system decodes it, breaks it down, analyses it and, depending on the patterns found, encodes a response that it sends back to its interlocutor. A clear example of this function is the well-known chatbots. If we ask a question to a text-based interaction system, it will look for correlations in the words we provide that it has been programmed for, and will produce a response based on the analysis of that combination of words.

Verbal interaction: this second case has the same basis as the previous one, with the added complication that the system has to recognise the sender’s voice, convert it into text, and follow the decoding and pattern-search procedure described. Once the message to be conveyed has been determined, the text is converted back into speech to deliver the response. Siri, Alexa, in-vehicle voice controls, or telephone support systems are everyday examples of this type of technology. We ask them something we want to know or give them a specific command, and they return a response based on how they have interpreted the initial request.

Text identification and classification: Natural Language Processing systems are also capable of identifying texts based on the words they contain. In this way, they can locate parts of a document in a long text (law, decree, thesis, etc.) or identify the type of document by its content. In the latter case, the example of spam email is very illustrative. NLP systems identify certain predefined words and expressions, and based on them determine whether it is junk mail or assign it to the inbox.

Translation: Another of the most widespread NLP applications is automatic text translation, which we have all used online and which provides responses in hundreds of languages. A new generation of translators displays the content of posters, signage and any type of text you want to translate by capturing an image of the text to be translated with your mobile phone.

As we can see, our interaction with machines is increasingly part of everyday life and takes place across all areas and sectors. Digital transformation is here to stay and to make certain tasks easier for us, and in this evolution Natural Language Processing has a great deal to contribute, since language itself is our most direct form of communication.