The natural, spoken language of humans is the most direct and easiest way for communication and exchange between people. If we want to communicate with machines, we need technical tools such as mice, trackpads, keyboards and the like.

Natural Language Processing offers an alternative.

In addition to our natural language, we have also developed programming languages and codes. Programming languages are also languages, but unlike natural language, most people are unfamiliar with them and cannot understand them intuitively. For this reason, natural language was at the centre of the development of interfaces between man and machine very early in its history.

This led to the development of a whole field of research and development: Natural Language Processing, abbreviated to NLP.

Note: The abbreviation NLP is also used for Neuro-linguistic Programming.

What is Natural Language Processing?

Natural Language Processing – or NLP for short – is a subject between classical linguistics and computer science. Expertise from both areas had to come together to make significant progress. On the one hand, it is necessary to understand the formal structure of natural languages and, on the other hand, to transfer this functionality to technical systems. Many universities now have an independent department for this purpose: Computational Linguistics.

The analysis of natural language is always based on the smallest, “meaning-distinguishing” unit.

NLP is thus based on an understanding of language that divides language into several levels. The analysis of natural language is always based on the smallest, “meaning-distinguishing” unit. By comparing these units, a logical system is gradually created that describes a natural language.

The two words “house” and “mouse” are decided logically by the phonemes “H” and “M”. These two can thus take on a meaning-distinguishing function.

Differentiations such as these can be made at all possible levels of language until they finally result in ever larger correlations. The following structural linguistic elements have a function that discriminates between meanings:

  • Individual Sound Letters as the smallest linguistic unit (“house” vs. “mouse”)
  • Segment analysis for word recognition (“Igohome” vs. “I|go|home”)
    Morphological analysis to recognize the endings (“ask” vs. “asked“)
  • Syntactic analysis of sentences and sentence structures (“He promised to buy me a new car every year”) vs. (“He promised to buy me a new car every year.”)
  • Semantic analysis of meaning (“Golf = sport” vs. “Golf = car”)
  • Dialogical analysis of communication (sender – message – intention – receiver)


Natural Language Processing now assumes a simple context. If it is possible to first build up an understanding of the smallest linguistic structures, more and more complex correlations can be understood. So in order for machines or programs to understand complex linguistic structures, they must first – like a child – learn to understand the simple linguistic structures.

Natural Language Processing and Artificial Intelligence

When it comes to adaptive programs, we are in the field of artificial intelligence. Significant progress in the field of Natural Language Processing was not achieved until the use of artificial intelligence had reached a certain degree of maturity. Artificial Neural Networks and Machine Learning methods such as Deep Learning contributed to this. Very complex facts, such as those provided by the natural language of humans, can therefore be understood by machines.

Learning methods such as transfer learning are particularly interesting, as they make it possible not to always start from scratch. This means that once the basics of the language have been understood, individual solutions can be build on them.

The fact that Natural Language Processing is not exclusively about language comprehension, however, was made clear by Google by presenting the Google Assistant “Google Duplex“. Natural speech output is also part of the goal of human-machine communication and interaction.

The fields of application of NLP are versatile

The spectrum of possible applications is immense. It exceeds the human-machine communication mentioned above. On the way to an even better understanding of language, numerous application areas have opened up in which Natural Language Processing has also developed new perspectives.

  • text mining and structure big data sets
  • intelligent evaluation of text and media data
  • intelligent word processing (autocorrection, conversion from speech to text)
  • translation programs
  • chatbots and digital assistants
  • generation of speech (speech output)
  • language as an interaction medium for controlling machines
  • and more…

For some examples, you can have a look at hyScore’s use case section.


In addition, Natural Language Processing opens up numerous application areas for users without special knowledge of programming languages, for example, or for people with disabilities, such as production machines that can be controlled via voice input.

In this respect, NLP also plays an important role in the transformation of the world of work.

More information about Natural Language Processing can be found in our article series NLP Insights.