Natural language processing | Régens NLP AI Solutions

What is natural language processing?

Natural Language Processing (NLP) is considered a subfield or branch of artificial intelligence (AI) and computational linguistics that focuses on the interaction between computers and human language. More specifically, giving computer programs the ability to understand (as much as is possible for computers to understand in their own way) text and spoken words almost on the semantic level that human beings can. The primary goal of NLP is to enable machines to comprehend, interpret, and generate human language in a way that is meaningful and useful.

NLP combines the aforementioned computational linguistics with statistical, machine learning and deep learning models. The combination of these elements allows computers to process and ‘understand’ the complexities of human language along with the original intent and sentiment. Through the use of machine learning, more specifically neural networks, large amounts of textual and voice data are used to train these models, enabling them to learn patterns, structures, and nuances in language.

IT guy analysing data and developing AI solutions that are based on natural language processing

How does NLP work?

The textual and voice data that is used to teach the models go through a critical step that constitutes Natural Language Processing, referred to as the pre-processing phase. An nlp technique that is part of many machine learning methods, pre-processing is the phase where the cleaning and transformation of unstructured text and voice data takes place, preparing it for further analysis. This phase is made up of 4 main subtasks: 

Ai technology using deep learning to develop natural language processing
  • Tokenization: Tokenization is the process of breaking the raw text into smaller chunks called tokens, which are the basic building blocks used for further analysis. These tokens can be either words or units, and help in understanding the context by analysing the sequence of the words. Tokenization can be as simple as splitting the text by whitespace, but more advanced techniques may be used.
  • Text Cleaning: This task involves removing any irrelevant or noisy elements from the text. This commonly begins by converting all text data into lowercase or uppercase to ensure uniformity, followed by removing special characters, punctuation and numbers. Finally, the task is concluded by removing stop words, which are common words that do not carry any significant value that contributes to the comprehension of the text and can be safely removed. This way, only unique words that offer the most information about the text remain, a process not unlike entity recognition.
  • Lemmatization and Stemming: This step aims to reduce inflections and variations of words to their base or root form. Stemming removes prefixes or suffixes from words while Lemmatization goes beyond and ensures that the root form is a valid words. It leverages language-specific knowledge to obtain the base dictionary form of a word.
  • Syntactic analysis: Also known as sentence parsing, this step deals with assigning each individual word a class (classification or part-of-speech tagging), combining them into word groups or ‘phrases’, ultimately establishing syntactic relationships between different word groups.

Natural language processing plays a crucial role in enabling natural language understanding for machines, facilitating interactions with humans more naturally and effectively, contributing to the development of applications that understand and respond to natural language queries. Its growing role in enterprise solutions is of an ever-increasing value, helping to streamline business operations and increase employee productivity while simultaneously simplifying mission-critical business processes.

Businesses have a vast amount of textual information from several data sources at their disposal, but deriving information from such large volume of data can be challenging. Natural language processing-based AI solutions provide the opportunity to gain valuable insights and relevant information from textual data.

NLP technology can process human language and analyze unstructured data sources like emails, social media, customer feedback, and call center conversations. This technology enables businesses to identify patterns and trends, classify and categorize data, and identify key phrases and sentiments. With NLP, businesses can gain a deeper understanding of their customers’ and partners’ needs and preferences, which is crucial for delivering personalized services and products.

One of the benefits of Régens’ NLP services is that they can be highly customizable. Régens can create solutions tailored to the specific needs of each business, with user-friendly interfaces and unique features. This opens up a world of possibilities for businesses and enables them to analyze data from different regions and markets in multiple languages, making it easier for them to streamline their workflows and adjust their strategies accordingly.

Why is NLP important? 

An advantage of Régens’ NLP services is that it can automate the process of analyzing and extracting insights from large volumes of data. This automation can save businesses time and resources, allowing them to focus on other areas of their operations. Real-time analysis also helps businesses to stay on top of their data, making it easier to make informed decisions and adjust strategies on the fly.

In summary, Régens’ NLP service is a valuable tool for businesses looking to gain insights from their textual data, improve their decision-making, and enhance their customer experience. NLP applicaions are vast and continually expanding, revolutionizing how businesses operate, communicate, and deliver products and services. Using NLP effectively can provide a competitive advantage and improve efficiency, leading to better customer experiences and business outcomes.

researcher using Natural Language Processing models

Natural language processing examples | Common NLP tasks 

Using Natural Language Processing, Régens has developed several AI solutions that can be easily implemented into the roadmap of various businesses throughout a multitude of industries, helping them optimize their workflows and allocate resources in an optimal way. These are some of the most unique NLP solutions that Régens has to offer:


This technology converts written text into spoken speech, enabling computers and devices to read out written content and allowing users to listen to information rather than reading it, in turn creating a more accessible and interconnected world.


This particular sort of technology could be considered as an extension of STT, with its goal being a more specified function. Its aim is to identify and verify the identity of an individual based on their unique vocal characteristics. This is done by analyzing various features of the person’s voice, such as pitch, tone, intonation and pronunciation.


Speech to text is a sibling to TTS technology, serving a similar purpose and converting speech or audio input into written text, allowing for the transcribing of spoken language into text and facilitating communication for individuals with speech impairments.

Sentiment analysis is the process of determining the emotional tone or sentiment expressed in a piece of text, such as positive, negative, or neutral. Régens has taken it one step further and allowed for the analysis of sentiment in vocal data, opening up new opportunities for businesses everywhere.

This technology leverages the use of artificial intelligence and computer algorithms to automatically translate text from one language to another without human intervention. Due to its deep learning nature, it is also constantly analyzing new data that has been provided to it and is able to increase its proficiency and accuracy. It enables efficient and rapid language translation, facilitating cross-lingual communication and breaking down language barriers in various domains.

This technology automatically identifies and extracts the most important and relevant words or phrases from a text that best represents its main topics and content. The applications of a keyword extractor tool are abundant and should not be overlooked.

Pricing options to start using Natural Language Processing

To obtain a quote for our models for Natural Language Processing (NLP) technology, please reach out to our dedicated team, who will be more than happy to assist you and provide you with detailed pricing information based on your specific requirements. We strive to offer competitive and transparent pricing that reflects the value and capabilities of our advanced Natural Language Processing solutions. We understand that each project and requirement is unique, and our pricing structure is tailored to accommodate specific needs and usage scenarios.

Contact us