Natural language processing, also known as NLP, describes the machine processing of natural language. NLP is a sub-field of artificial intelligence (AI). Humans are more and more frequently coming into contact with AI in their daily lives – whether with Alexa at home, with OK Google on their smartphone or when making a call to customer support. Today, humans are speaking more often with machines. And the areas of application of NLP are steadily on the rise.
A typical interaction between humans and machines is as follows:
Natural language processing signifies teaching the machine how to understand language. But this involves much more than a dictionary in the form of a database. Other aspects, such as situational context or sentiment analysis play a part in NLP. In practice, the meaning of spoken or written word can only be understood when various other criteria are taken into consideration. Ideally, this makes the interactions between the user and the application more natural.
The abbreviation NLP stands for natural language processing as well as neuro-linguistic programming. Apart from the common abbreviation these are two entirely different terms.
Companies use NLP techniques to noticeably improve their customer support. Large quantities of data are needed to ensure that natural language processing, in conjunction with artificial intelligence, can cover an increasing number of service areas. This is where big data and the continuous analysis of the communication come into play. As a result, telephone, chat or email support can be processed more independently. NLP can also be very useful for analyzing customer comments.
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Customer service systems based on chatbots are less expensive than human-based support staff. They work fast and, today, many customers even take them for granted.
The naturalness of speech recognition is NLP’s most distinctive feature. This is where the fundamental principles of artificial intelligence and machine learning come into play: The algorithms of speech analysis programs learn from their own experience. They compare positive and negative results. In doing so, they gradually improve their responses.
While straightforward speech analysis finds the best content-related answers to similar questions, NLP provides additional components: NLP finds context-consistent as well as emotionally effective answers.
Finally, based on a customer’s previous actions, chatbot NLP systems also supply accurate information about the customer’s future behavior. This aspect makes NLP interesting for marketing strategies. By analyzing the mood on social media, including Facebook or Twitter, it can determine a customer’s attitude towards a company, a campaign or product in real-time. Based on specific responses, this establishes entirely new options: Strategies can be reconsidered or newly developed. This makes NLP a driving force for AI and machine learning.
NLP’s difficulties lie in the nature of the matter: Speech does not always follow strict logical rules. It is controlled by emotions and changes frequently – depending on the situation in which it is being used. It is extremely difficult for an algorithm to recognize sarcasm, irony or hidden criticism. Verbal data are unstructured – yet they are the foundation for programs that work solely according to logical rules. Based on the principle of trial and error, software gradually recognizes special context and therefore learns how to avoid false interpretations – for instance in translations.
The more standardized and structured the data are, the easier digital systems can process the data. Human language is complex by nature. The rules of human communication are schematic and loosely structured. This is where NLP comes into play and attempts to identify these structures.
Whether chatbots, telephone and email customer support, filtering spam messages or the development of dictation software: NLP greatly enhances the skills of AI systems. Chatbot NLP systems are especially useful when communicating with customers. The rule here is: The larger the data basis, the more accurate the results are.
Dieser Artikel wurde am 27.May 2019 von Jan Knupper geschrieben.