The NLP reality check
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.
A brief introduction to natural language processing (NLP)
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.
Where NLP is used today?
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.”
Someone types the following search query into Google: When is the next flight from Zurich to Amsterdam? Tokenization now initiates a process in which the entered text is chopped up into smaller elements, such as sentences or words, and then segmented.
In the example above, the program determines that:
when = the answer must be a date
flight = is an airplane
Zürich, Amsterdam = are airports
The database is then searched for upcoming flights from Zurich to Amsterdam and the user is shown the results.
However, NLP can also be used to interpret free text so it can be analyzed. For example, in surveys, free text fields are essential for obtaining practical suggestions for improvement or understand individual opinions. Before deep learning, it was impossible to analyze these text files, either systematically or using computers. Each answer had to be read and evaluated individually. Now, with NLP, an unlimited number of text answers can be scanned for relevant information and analyzed or classified accordingly. The ECHONOVUM INSIGHTS PLATFORM also capitalizes on this advantage and uses NLP for text analysis. Sentiment analysis (sentiment mining) shows which comments reflect positive, neutral, or negative opinions or emotions.
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 future of NLP
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.
NLP allows companies to continually improve the customer experience, employee experience, and business processes. Organizations will be able to analyze a broad spectrum of data sources and use predictive analytics to forecast likely future outcomes and trends. This, in turn, will make it possible to detect new directions early on and respond accordingly.