Natural Language Processing

Natural Language Processing (NLP) is the processing of human language by a machine. In today’s everyday life, people talk more and more often with machines – for example, with computers, smartphones or chatbots on websites. NLP optimization is a subfield of artificial intelligence.

Natural Language Processing makes spoken language understandable to machines. NLP captures the meaning of spoken language and generates a response in response. This reaction is often a speech output itself. NLP is therefore the interface between humans and computers. Machines are learning to understand humans better and better – mainly thanks to Artificial Intelligence.

Artificial intelligence is based on a large amount of data and uses it to recognize structures and correlations on its own. In order to analyze speech, it requires a large amount of audio data in particular. Machine learning follows the principle of trial and error. The systems thus improve not only through their correct analyses, predictions and responses, but also through their incorrect ones. Everyone who uses language assistants simultaneously contributes to the constant improvement of these systems.

We encounter NLP in many situations today

  • Voice assistants such as Alexa or Google Assistant.
  • Automated customer support at hotlines
  • Chatbots on websites (“Hello, my name is Julia. Can I help you?”).

Note: The abbreviation NLP is used not only for Natural Language Processing, but also for Neurolinguistic Programming. However, apart from the abbreviation, the two fields have nothing in common.

How does Natural Language Processing work?

NLP captures spoken language and recognizes its meaning. At least five areas of linguistics play a role in the machine processing of language:

  • Morphology (composition of words)
  • Syntax (meaning and structure of sentences)
  • Pragmatics (contextual meaning of language)
  • Semantics (meaning of words and sentences)
  • Phonetics (properties of different speech sounds)

Practiced NLP works in a simplified way according to a five-step principle:

  • The program stores spoken language as an audio file.
  • This audio file is converted into text.
  • Based on the text and a variety of data, algorithms capture the meaning of the words in their context.
  • Based on this analysis, the program generates a response, which in turn is converted into text and then into spoken language. However, the response can also be non-linguistic, depending on the application.
  • The machine (computer, smartphone or web application) outputs this reaction.

The difficulties lie mainly in these points:

Converting speech to text. This is where machine learning is primarily used. Artificial intelligence uses a large amount of training data here, with which the program gradually achieves finer and finer differentiations and, for example, also recognizes dialects.
In order to understand the meaning of a statement, extra-textual elements also play a role. This is, for example, the situation in which the input is made. The more data is available about this, the more accurately a response can be generated.

The biggest challenge for NLP is the complexity of natural language. It is clear that Artificial Intelligence and Machine Learning also have their limits here. Humor, irony, sarcasm, rhetorical questions or paradoxes are very difficult for algorithms to recognize.

Application areas of Natural Language Processing

NLP as an interface between the machine and the user makes the handling of technical devices easier and easier. NLP also plays a major role in the marketing sector. For example, offering a chatbot for individual help on a website has a very sales-promoting effect. Many users take this service for granted. Many people find communication with a machine pleasant because there is a greater inhibition threshold compared to real people.

NLP can also be used profitably in customer support. With increasing standardization of inquiries and answers, there are more and more areas of application – without having to do without additional human support. Human manpower can be used in a much more targeted way thanks to NLP.

At the start of contact, an NLP system works to identify the customer’s concern – and, with the help of sentiment analysis, their mood. The system can then make decisions, such as:

If it is a typical standard question, the customer continues to communicate with the computer. This results in a quick and efficient solution to the problem. This is in the interest of both the customer and the company.
If the question is complicated or the system notices a bad mood on the part of the customer, the conversation is passed on to a human processor as quickly as possible in order to find an individual and personal solution to the problem.

Why is Natural Language Processing so important?

NLP is especially important because users of mobile devices and virtual assistants have already become accustomed to speech recognition and take this capability for granted in other situations. Verbally spoken commands take less time than keyboard input. Devices with powerful NLP systems therefore have a clear competitive advantage. This also applies to websites that offer users a chatbot.

On the other hand, NLP also presents website operators with new challenges – for example, in content marketing. The keyword here is “content for voice search” – in other words, content specifically geared to voice search. In addition, long-tail keywords are playing an increasingly important role in search engine optimization of content, because entering more complex search terms is much easier with voice control than with the keyboard.

People talking to machines is no longer a dream of the future, but has long since become reality. Thanks to Natural Language Processing, it is becoming increasingly easy to get a quick answer to questions or to operate apps on smartphones. Increasing computing power and the large amount of data available worldwide (Big Data) ensure that the results of NLP are getting better and better.

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