Combining computing technologies with human language has become a revolutionary force for modern science. At the heart, or rather the brain of this strategy is natural language processing or NLP.
Have you ever thought about how frustrating it would be to conduct a Google search and not get a satisfactory result? What if virtual assistants or chatbots suggested something utterly contrary to what you requested? How confusing would your inbox be without spam filters?
These tools represent only a fraction of the power of natural language processing (NLP), a form of artificial intelligence with promising applications.
For businesses, the ability to process speech and written words in real-time can be crucial. It can automate tasks that previously required careful manual analysis, better understand the feelings of consumers and employees, and analyze qualitative data.
Read on and find out what NLP is, how it works, and what it can do for you!
What is NLP?
Natural language processing is a branch of Artificial Intelligence (AI) dedicated to understanding the nuances of human language.
Simply put, it is the ability to process and understand language, both written and spoken.
In addition to understanding language, NLP also enables devices or systems to create responses through text and audio for human-machine interaction.
Humans and technology communicate very differently. An intermediary is needed to make this interaction possible. Above all else, communication needs to be fluid; we want to avoid that feeling of interacting with a “robot.”
This is one of the many reasons that AI has become critical in business, and NLP is seen as an important growth area for many strategies.
According to data from IBM’s 2021 Global AI Adoption Index report, a third of companies are using AI in some way, while 43% are using it extensively.
The report also pointed out that Natural Language Processing is among the top reasons companies are adopting AI. More than 50% of companies that have adopted AI are using NLP-enabled applications or systems.
The expansion of artificial intelligence is not surprising, considering the immense benefits it offers companies. Benefits such as increased efficiency, productivity, reduced costs, and increased financial results, among others.
How does natural language processing work?
Human speech recognition can be divided into different areas. These represent steps, which are later used for general text recognition, such as:
- Language recognition;
- Classification of individual words and phrases;
- Acquisition of grammatical information;
- Identification of individual word functions within a sentence (subject, verb, adjective, etc.);
- Interpretation of sentence meaning;
- Understanding sentence context and relationships.
The steps involved in natural language processing begin with accessing data in its original form (a message written in a database, for example) and a language basis for comparison.
Once the data is collected, the information is split using various data pre-processing techniques.
What are the main NLP techniques?
For organizational purposes, we can divide the Natural Language Processing area into three main sub-areas: sentiment analysis, intention analysis, and information categorization.
- Sentiment analysis: consists of using NLP tools to extract the sentiment of a sentence or sentence. Generally, binary classifications are used, such as positive or negative.
- Intent analysis: seeks to identify the intent and meaning behind a given text or speech.
- Categorization: aims to separate content into separate classifications.
But it doesn’t stop there. There are also three different types of techniques used in the natural language processing disciplines. The most common are:
- Deep neural networks, used to model information and determine a preferred outcome in a particular scenario.
- Machine learning and other traditional AI approaches that rely on the use of “training data” to make decisions based on statistical methods.
- Rule-based techniques, which make decisions based on a certain set of parameters.
Major Uses of Natural Language Processing
Machine learning dynamics coupled with natural language processing allows a computer to understand, analyze, and simulate human language. Here are some of its most common applications. Check it out!
User Emotional Analysis
The feeling analysis area consists of using NLP tools to extract feeling out of a sentence or speech. Generally, binary classifications are used, such as positive, negative, or neutral.
An organization can adopt it to understand whether, for example, mentions of the company on social media are positive or not, and then mobilize the public relations department to improve these results.
Virtual Assistants
Virtual assistants like Siri, Alexa, Google Assistant have become increasingly popular.
Many people don’t know that virtual assistants are built on NLP principles to understand people’s requests and are even capable of developing conversations.
Communication occurs by identifying speech patterns through voice recognition. Next, virtual assistants deduce the meaning of the interaction and provide helpful answers.
Chatbots
Investing in chatbots is an excellent choice for companies looking for new ways of interacting and engaging their customers. Effective communication between bot and user gives clients a new possible path to resolve their problems at any time of day.
Bots use artificial intelligence features such as NLP and machine learning to provide answers to questions and problems.
NLP has provided chatbots with increased sophistication, using complex algorithms to interpret user queries and respond naturally.
Search platforms
Another great use, which Big Techs have been taking advantage of in recent years, is in the internet search market.
Google, for example, adopts NLP algorithms to understand users’ search intent and target the pages that best meet their search objectives.
Also, have you ever noticed that Google tries to predict what you’re searching for by suggesting completed search inquiries? That’s NLP in action, identifying what you are trying to say.
Spam
Another everyday use of NLP that affects us daily is spam filters. It sounds trivial, but they use NLP to identify spam characteristics in text and titles.
In addition, these platforms’ management systems use intelligent methods to divide emails into categories.
Machine Language Translation
Translation is one of the oldest NLP applications. Translating texts from one language to another involves complex knowledge. Thanks to language processing, this translation has become more accurate than ever before.
Complex and challenging texts are being translated more accurately with NLP, making international communication even more accessible.
