What is natural language processing?
Hey, curious reader! If you've landed on this page, it means that for one reason or another you're interested in natural language processing – or NLP. In this article, we'll learn more about what it is and how it works.
But to understand what natural language processing is, we need to take a step back and see which macro categories it belongs to.
A definition
What is NLP – and what is it about?
NLP is a branch of computer science – and specifically a field of artificial intelligence (AI). It is based primarily on us and our reality, and above all on our most important means of communication: language. It then links language with statistics, machine learning and deep learning. By combining linguistics and computer science, NLP aims to decode language structures and rules. The goal is to build models that can understand, break down and separate important details from text and speech.
NLP explained simply – with an example
To put it in simple terms: it's about enabling computers to understand text and spoken words in a similar way to humans. Does something come to mind? Yes, exactly! Your smart assistant is a concrete (and perfect) example of natural language processing put into practice. Machines that talk to us and answer us in a human-like way are already a reality – and one that gets better every day. You can read more on this topic here.
The history of NLP: from the beginnings to today
The concept of NLP goes back a long way. As early as the 1950s, experts were looking for ways to program computers for natural language processing. But it was only with growing processing power and the development of machine learning that the field made rapid progress.
Its structure
When we think of natural language processing, we shouldn't forget that human language is extremely structured. Besides vocabulary, syntax and grammar, the building blocks of written language, there's also phonetics, tone, accent and pronunciation in spoken language.
Each of these elements matters for NLP. One and the same word or sentence can have a completely different meaning depending on the speaker's or writer's context and intent. Essentially, language is sometimes hard to decode even for humans, so getting machines to understand us is quite a feat. But it isn't impossible.
Its benefits
In a way, natural language processing helps bring structure to something that is unstructured by nature: communication, as you know, doesn't always run smoothly. So the goal is to give it a clear line, which can lead to smarter software and even help us communicate better with other people.
NLP therefore helps improve interaction between humans and computers by providing detailed insights into intentions and opinions. Companies, customers and technology users alike can benefit from these factors.
How does natural language processing work?
Why social media is a valuable data source for NLP
One important source for natural language processing is social media. Why? Quite simply: think about how social media is used. Every day, people interact with each other and exchange large amounts of freely available data. This data is extremely useful for natural language processing to understand human behavior and customer habits. Data analysts and machine learning experts use this data to teach machines how to imitate human language behavior.
Greater efficiency in customer service thanks to NLP
A direct consequence of this learning is saving money and time. With a system that can respond faster to several inputs at once, no additional employees are needed to handle the requests on the other side.
NLP in everyday life: more widespread than you think
In fact, NLP is much more widespread than you think. You enjoy its benefits without even knowing it. Did you know, for example, that autocorrect is a tool based on NLP? Plagiarism checkers also work thanks to natural language processing.
NLP is a puzzle
But as we've already mentioned, the technology behind natural language processing is complicated and multi-layered. The starting point is an article or document – in any case a text file. This in turn is the key to how the algorithm interprets and functions. However, this algorithm must be easy for the machine to understand. So let's look at what happens step by step.
1. Segmentation.
The first step of NLP is segmenting the text. The text is broken down into its individual components. Punctuation can be taken into account, i.e. commas and periods.
2. Tokenization.
The next step consists of further information gathering that's even more detailed than the previous one. Individual words are extracted from the already divided sentences and stored in the system's memory.
3. Removing stop words.
The words that don't add any content to the text are removed: the so-called function words, such as "and", "the", "is". They have no real meaning of their own; rather, they ensure the cohesion of the text.
4. Morphological analysis.
For each of the selected words, the root form is isolated and the system is taught to recognize it. Next in natural language processing, the machine learns that there are prefixes and suffixes that determine the meaning of a word. "-ing", for example, can form a noun such as "processing", while the verb is "process" and "processor" is formed with "-or". The system therefore memorizes the relationship between root forms and derived forms.
5. Syntactic & semantic analysis.
This is the point where the machine is taught the structural function of the words in the sentence. This leads to semantic analysis, namely assigning a meaning to each element of the sentence.
6. Dialog and discourse analysis.
In one of the final steps of NLP, context and contextualization come into play again. Now the system learns to recognize the relationships between the different elements of a dialog, e.g. the answer to a question, a statement and its justification, and so on.
7. Building an identity.
Finally, natural language processing assigns a personality and acculturates what has become the foundation for a bot of various kinds. The bot learns to speak and gets feelings. At the end of the day, you could say the system has been trained: first it learned the rules, then it learned how to apply them, and finally it put them into context.
This last step, although it can be summed up in a few words, is actually more complex than it seems. To implement it, software developers use many machine learning algorithms – such as Naive Bayes, Random Forest and others.
Natural language processing – conclusion
In this article, we've looked at natural language processing (or NLP, if you prefer). We've given you a definition of NLP and explained how it's structured and how it can be applied.
Natural language processing is therefore a tool that combines computer science (especially AI) and linguistics. Its goal is to imitate human language behavior.
This happens in several steps with the important support of linguistics, which becomes the key to interpreting the bundle of information the machine wants to learn.
