NLP and Machine Learning: an ever closer connection for new communication

I progressi dell’NLP

We have all heard of the concept of artificial intelligence, a technology that supports us in everyday life and that has become an invaluable ally for companies. In particular, AI ​​analyzes feedback, provides automatic customer support, speeds up processes, etc.

However, artificial intelligence is divided into many forms and concepts, and one of the most important is NLP (natural language processing). It may be a technology little known to some, but we use it every day. Virtual assistants, such as Alexa and Siri, for example, base their operation on natural language processing.

Particularly in recent years, NPL has spread like wildfire in all business sectors. In what follows, we will explain how to apply NLP for your specific needs, a technology in which we at Humable have become experts.

What is natural language processing?

NLP e Machine Learning

NLP refers to the interaction between computers and human language, which is analyzed and understood by sophisticated algorithms. In addition to the aforementioned virtual assistants, NLP technology is present in spam filters, voice messages, automatic text completion, spell checking and related keywords during searches, etc.

The computer, based on the context and previous learning, is able to correct or identify the most suitable word, according to its needs. In fact, it does what a human being could achieve, but in the case of the latter, there is greater expenditure of resources and time.

NLP technology can process an extraordinary amount of data in just a few seconds, an operation that perhaps would have taken weeks or even months for human intelligence. Thanks to their extraordinary abilities, NLP systems quickly ended up in company mechanisms.

The advantages of NLP in companies

Marketing is a fundamental concept for all companies, but its main objective is not so much to sell as to communicate. There is continuous communication between employees, customers and companies, whose data should be extrapolated and analyzed to improve interactions. Just think of all the data that is exchanged before, during and after a sale. Well, NLP technology can do this in a short period of time and without any effort.

One of the most popular examples of NLP is represented by chatbots. Most business and e-commerce sites have small chat windows, usually in the lower right hand corner, through which customers can request information or ask questions. Behind that window is not always a human being, but rather a machine that perfectly performs the same tasks as a person. Over time, it is “trained” to answer increasingly frequent questions and, because of machine learning, it learns new notions.

Another increasingly widespread technology in the field of natural language processing is automatic translation, which has made great strides in recent years. In the light of an unstoppable globalization process, for a multinational company, or a company that aims to cross national borders to reach new markets, it is essential to communicate quickly and directly with potential partners or foreign customers.

Yet another example is the analysis of polls. NLP systems improve the customer experience by suggesting the most frequent words when searching for a product or service, but also that of companies. In fact, they are able to analyze the data and trends of keywords, suggesting the main trends of the moment and providing useful information on customer preferences to better target them.

machine learning: the main applications

machine learning literally means automatic learning, and indicates the ability of machines to learn without having been previously programmed. Robots act like “sponges”, capable of absorbing data and information and then processing, interpreting and reusing this according to the context. machine learning has to do with language, therefore with natural language processing.

Searches on search engines are dictated by machine learning systems, which, through specific algorithms, provide a list of relevant results. Other examples of machine learning are email spam filters, which intercept unwanted messages and send them directly to spam. In the financial sector, this system is essential to block fraud and data and identity theft.

machine learning has found widespread application in scientific research, managing to diagnose tumors or rare diseases to prevent them and intervene in good time. Still other examples are voice recognition, handwriting identification, and automatic guidance systems.

Our solutions facilitate automatic learning by machines, which are able to perform increasingly complex and sophisticated actions. At Humable, we help you use artificial intelligence to create increasingly sophisticated communication models with customers. If you want to know how to minimize errors, increase productivity, and turn data into value, contact us.

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