When will NLP finally arrive?

Has the machine decoded human language yet? How Natural Language Processing (NLP) is used today and what it is capable of tomorrow.

Wouldn’t it be great if you could simply hold your smartphone to your mouth, say a few sentences, and have an app transcribe it word for word? Google’s Voice Assistant has already achieved positive results for English-speaking users. In German, however, the results are not quite as exhilarating. Or what if you could say something to your smartphone and it would respond in kind, allowing you to have an actual conversation with your virtual assistant? Try it out with Siri, and you’ll see just how far we have yet to go. Various websites currently feature chatbots that answer users’ questions about products and services. However, these bots don’t always offer the best customer experiences.

There are still no reliable apps on the market that can accurately determine the context of any given question 100% of the time. But it won’t be long until natural language processing (NLP) can decipher the intricacies of human language and consistently assign the correct context to spoken language.

NLP is a computer program’s ability to understand natural language in precisely the way it is spoken or written. It automatically processes text and spoken language. It is used in the field of artificial intelligence (AI) technology. One of the earliest and simplest NLP applications is spam detection. A computer program decides whether a message is spam or not based on the subject line and text in the email. The basis of this application is a rule-based approach, in which certain words, phrases, or sentences are fed into an algorithm, which then filters out messages accordingly. The current NLP approaches are founded on machine learning or deep learning: a computer acts based on training models. Using countless examples, the machine determines, on its own, the meaning of a text. The basic idea is for a computer program to learn a language similar to the way a child would.

The challenge facing NLP applications is that algorithms are typically implemented using specific programming languages. Programming languages are defined by their precision, clarity, and structure. Natural language, however, is anything but precise. It is often ambiguous, and linguistic structures depend on complex variables such as regional dialects, social context, slang, or a particular subject or field.

NLP is frequently used to process human language for search queries. Human beings are accustomed to acquiring information in a certain way: they ask questions. And they want to search for datasets on computers in the same way — the user enters a question, and the computer determines the most important elements of the phrase or sentence, then matches it with certain aspects of an existing dataset before displaying the results. In the field of NLP, this is referred to as “tokenization.”

In the field of IoT (Internet of Things), voice control is used to trigger an action when a precise, spoken command is given: the oven is pre-heated to 400°F, the shutters are lowered, the heat is set to 70°F.

Machine translation applications such as Google Translate or DeepL also utilize NLP and deep learning. In these cases, the system interprets the meaning of texts and transfers this meaning into another language.

The virtually unlimited number of new online texts being produced daily helps NLP to understand language better in the future and interpret context more reliably. Soon, users will be able to have a relatively meaningful conversation with virtual assistants. And perhaps one day a virtual health coach will be able to monitor users’ physical and mental health.

There is a wide range of new or improved areas of application for companies: a virtual assistant can help sales employees serve customers, or can listen to meetings, record the minutes, and store them in the CRM system. It can coach employees during customer acquisition and offer feedback on which questions and vocal modulations elicit positive reactions. Chatbots can answer customer calls, respond to questions, or transfer calls to the right employees. NLP applications can write articles about specific industry topics, translate product catalogs, or correct corporate texts and presentations. NLP can drastically reduce the time needed to find information within companies. Using NLP applications on a company’s intranet, enterprise search, or even on the internet, employees can ask open-ended questions to retrieve information.

Related posts:

I had the pleasure of interviewing Akshay Bansal, an Author, Entrepreneur and an Inventor, credited with chemical engineering and MBA. After finishing his post grad, Akshay started his research on…

They say the human body is about 60% water. We need water to drink, to wash, to keep cool in the summer. Without water, there is no life. What does the phrase “bodies of water” bring to mind for you…

When delving into the world of literature, it can be a bit daunting at first, but soon later you will realize that the varieties of text serve different purposes and causes. Each novel has been…