NLP – what is it and why is it so important in chatbots?

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It is thanks to natural language processing (NLP) that modern chatbots are efficient interlocutors. But how is it that NLP provides such a high degree of understanding of the questions asked by users and at the same time correctly answered? What do high-quality bots need to function efficiently? What’s behind the modest-looking interface? Almost every internet user who is looking for information on the company’s website or on social media has probably met a chatbot or sex chatbot at least once. Dialogue with such a program is possible thanks to the NLP technology.

Natural language processing – definition.

Natural language processing (NLP) is a field of science that emerged at the meeting point of many disciplines. It mostly draws from the achievements of artificial intelligence and linguistics. The subject of research is the analysis, understanding and generation of natural language. Natural languages are those used by humans, unlike programming languages, for example. Natural language processing enables communication between people and a computer (program) without using programming languages such as JAVA, Python. What a person says or writes is input data to be processed by the computer. The system’s feedback is the output.

NLP often falls into two categories:

Natural Language Understanding (NLU)

Natural Language Generation (NLG)

Machine understanding of natural language: what does this mean in practice?

Since you can communicate with the program without knowing the code (programming languages), the group of users of a given system is significantly expanding. It can be basically anyone, of course, provided that the program is adapted to this particular language.

Where is NLP technology located and what is its application?

Today’s chatbots are based on NLP technology and it is thanks to it that they can understand user messages and react to them correctly. NLP chatbots are also often called “2nd generation bots”, in contrast to the 1st generation bots, which have not been positively received by the market. However, NLP is not only the chatbots themselves, but also their voiced versions, i.e. voicebots and voice assistants such as Google Assistant, Alexa, Siri or Bixby.

Why do chatbots and other conversational systems need NLP to work well?

On a daily basis, we do not realize how complex and rich in information our conversations and messages are, which we exchange with each other many times a day. Sometimes the complexity and multi-faceted nature of our communication do not become apparent until we begin to learn a foreign language. Every user knows their native language so well that they no longer subject it to analysis and reflection. It is completely different when we train a bot, whose task is to interpret the person’s statements as precisely as possible and to trigger the system’s behavior adequate to the situation (answer to a question or start action according to a specific scheme / scenario).

Natural language processing is not only about understanding words.

Everything we express, whether written or spoken, contains a tremendous amount of information that goes far beyond the meaning of individual words. Stylistics of statements, context, punctuation, phraseology, tone of voice and facial expressions: all this allows for an interpretation of statements much deeper than a simple analysis of the lexicon and grammatical rules. Of course, we would like chatbots to be able to interpret the entire complexity of human communication, but even without it – they cope better and better. Today, an NLP bot doesn’t just understand the meaning of words. Chatbots can perfectly interpret statements, decrypt alphanumeric strings, personal data, recognize names and surnames, proper names, addresses, telephone numbers. Adding to this the ability to put dialogue in context, you get a pretty good result in reading users’ intentions. These are very practical applications that are very useful in business and various types of organizations on a daily basis. NLP chatbots are used not only to conduct simple conversations, but also to handle entire processes.

Are all bots built on one NLP equally effective?

Chatbot service providers usually have their own NLP. Its quality has a great influence on the final effectiveness of the chatbot. However, not every bot prepared on the same tool gets the same results. The technology is only the basis on which the implementation is prepared. Much depends on the configuration work. Configuring a bot is nothing more than putting the potential of a given technology into practice. Skillfully written scenarios, well-edited answers and linking the facts together give the final result. An additional aspect that must be taken into account when verifying the effectiveness of the chatbot is the time that has passed since the production launch. New bots tend to be less effective than longer bots because conversational systems are constantly “improved” over the course of their use. On average, a few months after the production implementation, the effectiveness of such solutions is so high that they can operate independently.

Can NLP technology be so good as to compare the bot’s skills to human skills?

Work on conversational artificial intelligence has been going on for many years. In the 1950s, the Turing test was created – an exam that tests the conversational skills of a machine. And although many experts and enthusiasts of the AI industry set themselves the goal of creating a program that will eventually pass this test, contrary to appearances – this is not what business and institutions expect. We will certainly have to wait for the possibility of talking with artificial intelligence, which is indistinguishable from the human one. Nevertheless, some chatbots and voicebots are already able to handle 80% of processes in organizations, correctly answering more than 90-95% of questions. The basic determinant is, therefore, the correct fulfillment of the tasks assigned to bots and the implementation of selected KPIs. And with the requirements defined in this way, NLP bots today are able to deal with simple tasks on a level comparable to humans.