What is natural language processing (NLP)?

But what exactly is natural language processing? Which technology is it using? And lastly, how can you benefit from it? NLP brings together computational linguistics (a field of study that works on natural language understanding with computing) and machine translation that altogether make it possible for computers to process our speech, even proceed with a grammatical analysis. The technology is broadly used in computer programs for translation, GPS navigation and spoken commands. In the business environment, on the other hand, it is present in the field of customer support (voice assistants) or for the internal business operations.

There are 4 main segments of natural language processing: TTS, STT, STS and TTT. Don’t worry though, I will explain all these abbreviations and give use cases of all of them, starting with:

Let’s start with the NLP technique that generates text data into spoken words on the basis of digital documents such as emails, text messages, medical records or research papers. Through deep learning models, TTS algorithms analyze the proposed text and generate full sentences. Sometimes in a robotic style, sometimes with remarkable fluency and expression, making it hard to assess whether it’s a human being or a machine. Text to speech recognition software finds application in language learning platforms, virtual assistants, voice bots or in accessibility tools for visually impaired. If you’re curious on how it works, you can easily try it yourself: nowadays smartphones have the ability to read any portion of text data that you marked.

Now, it’s the other way around. Speech-to-text technology with the usage of machine learning algorithms, takes spoken words and converts them into a piece of text that is accurate to the context of the speaker and includes a sentiment analysis. Obvious example is Apple’s Siri technology or any type of dictation tool on your smartphone. Such machine learning methods are extremely used for generating auto-captions on Youtube or other platforms with videos. Its biggest advantage is convenience: speech-to-text enables us to do hands-free operations on our devices across many fields. If you think about it, STT has basically revolutionized the way we interact with our devices making it more accessible for all users.

PS. I highly recommend trying the dictation on your phone. Once I discovered it, I always used it for preparing longer messages or speeches. Some words could be twisted, so you may have to adjust it afterwards.

There is a high possibility that this language model is already in your household. Yes, I am talking about the beloved by many and feared by some personal assistants: Alexa, Apple HomePod, Google Home, you name it. All of them use the power of language processing and voice data. Based on voice commands that a user gives, an assistant processes human language, does the automated interpretation and then answers accordingly, so that it feels like human communication. Another example where such a model is used is the Google Translator, where a voice input is in one language, let’s say English, and after an automatic translation the output is in for example Chinese.

One note to that is, that technically the whole process is STT- processing of the input data-TTS, because in order to process the spoken language, first it has to be transformed into a digital text, analyzed and then generated into speech again.

Have you ever traveled somewhere abroad and had to communicate with a foreign person using a translator, because you couldn’t understand each other? Guess what! It was one of the NLP techniques and a 4th language model discussed today, text-to-text.

Such systems convert digital text into another digital text. Examples of TTT are commonly used online translation programs such as DeepL or Google Translate. These platforms convert written text almost instantly, because of NLP Algorithms that leverage deep learning and statistical methods. All of that to capture the semantic analysis of text and generate accurate translations of documents or even whole websites. Another usage of this technique can be found in all these artificial intelligence online tools that summarize or paraphrase text for you automatically.

Now that we’ve explored all the examples of NLP tasks in human language, it’s time to talk about how it could possibly be beneficial for your company.

Voice assistants, virtual agents, you name them. Trained to help your customers with repetitive tasks that process human language and according to the provided script, resolve issues. Voice bots already work across fields like banking, taxies or even medical clinics. Often they are the first contact point with a customer, boosting the efficiency of employees and saving costs.

This example may not be so obvious, but NLP tools are capable of classifying text and speech data to detect risky phishing or spam emails. The indicators here are misspelled individual words, threatening language or suspicious urgency. When it comes to corporate ransomware attacks, often it is an employee that is targeted first. Implementing this kind of tech could help avoid such unpleasant situations.

When we talk about artificial intelligence, there are endless possibilities. NLP technology in all its forms is present in our work life almost every day. One of the best things about it is that with your own training data and a team of qualified people, you can tailor your own language processing toolkit that will serve your company.