NLP
Hey Siri, what’s the nearest Italian restaurant? Hey Google, play a new playlist on Spotify. Nowadays, it is increasingly common to talk to your phone or to Google Home, for example. Our interaction with digital assistants relies largely on NLP (Natural Language Processing). Consciously or unconsciously, we encounter this machine learning technique almost daily. Aside from NLP being used by, for example, Google for Google Assistant and Google translate, you can also use NLP to classify data. In this article we describe what NLP is and in which situations we want to use it in our software.
What is Natural Language Processing?
NLP is conveniently the reason a computer understands human language. NLP brings together ideas from computer science, linguistics and artificial intelligence. Consider the systems that allow chatbots to communicate with customers in their own language. The customer’s question must be processed, interpreted, then an appropriate answer to the question is provided by the chatbot.
Artificial intelligence plays a crucial role in NLP because language is so specific, depending on how people think and express themselves. Artificial Intelligence (AI) helps process this into input that the system can recognize. By applying machine learning to Natural Language Processing, the chatbots are getting smarter and smarter and can recognize patterns and information from conversations better and better.
For example, NLP is a very important part of your e-mail box. Have you ever looked at the emails in your spam folder? Your mail provider uses NLP to scan and recognize the text and based on that determines if an e-mail belongs in your spam folder.
Language is not so easy
Human language is very complex and diverse. For example, not only are there thousands of languages in the world, but each language also has several sub-languages such as slang and slang. In addition, when we write, we all make spelling mistakes from time to time and do not always observe proper punctuation either.
The biggest problem with human language is that it depends on context. When you tell your Google Assistant that it’s “dark” in your living room, you don’t just want to communicate this, but the intention is for your digital assistant to turn on the lights.
In addition, we use synonyms and homonyms a lot in our language. Words such as sofa, arm, pillow or light have different meanings depending on the context. We have taught ourselves to recognize these. A computer brain obviously has to learn to do the same and does so through NLP.
In short, NLP divides natural language into its most elementary parts and tries to make sense of the interrelationships in order to filter a meaning out of the sentence or language as a whole.
Computer Lesson
With NLP, it is possible to teach a computer to classify descriptions into a category. For example, if you receive a brochure of a product with only a description, you can fairly easily classify what type of product the brochure belongs to. Using NLP, a computer can also learn this. In this way you can solve classification problems and automate certain tasks. So we can also use NLP to automate business processes.
NLP in Credit Management
A lot of e-mails are processed in the credit management department every day. If you send customers a reminder by e-mail, you often get a response by e-mail as well. In addition, many e-mails are sent internally about complaints or other tasks that need to be carried out. A large part of these e-mails can be handled automatically or a treatment process can be started using NLP. Examples include copy invoices, payment notices, bounced and out-of-office e-mails. Receiving, reading and processing a payment request received by email can easily take a few minutes. CreditDevice uses NLP to automatically send copy invoices, process payment notices or use a different communication strategy for incorrect email addresses such as bounced emails or long absences.
