The Complete List of Natural Language Processing in AI

by Dan Irascu

The Complete List of Natural Language Processing in AI

With the advent of artificial intelligence in our lives, the need for human-computer communication has arisen. The problem was the vastness and unstructured nature of human speech. However, through incredible achievements, the goal was achieved. The algorithms created have enabled computers to recognise and understand human speech in all languages of the world. 

Today, we use the Siri voice search engine, which is a result of natural language processing, without restriction. Surely every smartphone or computer user has already encountered an example of natural language processing, even if they didn't know about it. In this article you will learn what natural language processing is and how it works.


As Wikipedia says, Natural Language Processing is a general field of artificial intelligence and mathematical linguistics. It studies the problems of computer analysis and synthesis of natural language texts.

To put it another way, Natural language processing (NLP) creates programs capable of recognising, reading and responding to human speech.

NLP is essentially a point of intersection between artificial intelligence, computer science and linguistics. NLP is able to automatically process human speech, opening up a vast field of application. 

Today, natural speech processing is engaged in:

  • search engines (Google, Yandex, etc.);

  • chatbots;

  • voice assistants (Siri, Alice, etc.);

  • online translators, etc.

And these are just a few examples that underpin this area of artificial intelligence. Do you remember how convenient it is to translate entire websites into another language with a single click? Or how pleasant is it to switch on the lights in your flat using a voice command? That's entirely to the merit of NLP. As is already clear, natural language processing is a large niche, growing year by year


Let's explore how natural language processing works. In a general sense, NLP is comparable to learning a foreign language. Only in the case of automated computers, the amount of information viewed per unit of time is increased hundreds or thousands of times. While you have just entered a search query and opened the first website, the computer has already analysed all the information on the Internet on the subject.

The processing steps of the recognised text include entity extraction, syntactic analysis, semantic analysis, sentiment analysis and pragmatic analysis.


Entity extraction means splitting a sentence into parts in order to find the entity. When a computer processes human speech it tries to identify and extract key figures. The entity can be a person, a company, a country, a historical event, etc. In this kind of sampling, the computer analyses all possible Internet sources.

The main difficulty at this stage is weighing up all the possible options against the necessary ones. For instance, artificial intelligence searches for a person called "Marilyn Monroe". On the internet it is possible to find entries such as "Monroe", "Actress Marilyn Monroe", "Marilyn the Movies", etc. Therefore, it is particularly important to classify the entity you are looking for correctly at the entity retrieval stage.


The mention of "syntactic analysis" involuntarily brings back memories of school. Linguistics class, a test paper in which you have to draw arrows between words…

You were not wrong to allow yourself to make such associations. Creating chains of links between sentence words by artificial intelligence is an important part of natural language processing. 

Syntactic analysis is the most challenging and extensive component of natural language processing. Syntactic analysers divide an introductory text into parts and determine the structure of word relationships. The main point of the analysis is to determine the accuracy of the text being entered. For instance, the sentence "The hospital is writing a book" will be rejected by the analyser.


Semantic analysis is necessary to uncover the meaning of a text. After syntactic analysis, the semantic analyser makes meaningful connections between the words in the text. This is done first between individual words, then between word combinations. Semantic analysis also includes figures of speech and meanings in context.

Semantic analysis is often difficult for AI because the meaning of a sentence is not always unambiguous. For example, numerous words in any language are polysemous. If the text to be processed contains such a word, the semantic analyser will select the one that best fits the meaning of the sentence. In this way, the text is checked for meaningfulness and does not allow for phrases such as 'white petrol'.


The fourth component of natural language processing entails working with context. It is complicated for an AI to recognise the meaning of an isolated piece of text. Pragmatic analysis simply maps the actual objects/events that exist in a given context to the object references obtained in the last step (semantic analysis). 


Sentiment analysis allows artificial intelligence to identify and understand the opinions in a text. This analysis plays a huge role in many business areas. Interest in the topic of emotion recognition in text is heating up every year. But let's take it one step at a time.


The sentiment analyser works based on three main techniques:

  1. rule-based systems that perform sentiment analysis based on a set of manually created rules;

  2. automatic systems that rely on machine learning techniques to learn from the data;

  3. hybrid systems that combine both rule-based and automatic approaches.


Companies use InData Labs’ natural language processing for many purposes. For example, sentiment analysis can be used to recognise the level of customer loyalty to your brand.The data provided by the sentiment analyser gives a clear picture of the state of your company in the market. The sentiment analyser can be used to predict market changes, assess customer desires and much more. 

Let's say a company needs to find out its level of customer satisfaction. But no one has thought about printed questionnaires, right? The sentiment analyser looks at all available internet resources where the company is mentioned. In this way, we get an extensive analysis of customer sentiment about the company. For the same purpose, companies create chatbots to collect information about the customer's sentiment right on the website. Sentiment analysis will also help a company find out what its target audience is saying about a new product launch.

Today, consumers are faced with a huge choice of companies. In a fiercely competitive environment, it is important to give customers what the opponent cannot. A sentiment analysis can be used to improve a company's customer service. All incoming enquiries will be automated and checked for negative enquiries. This allows unresolved customer pains to be brought to the forefront. 

And of course, social media. If a company is socially active, sentiment analysis is a vital tool. Reactions to the posts, comments and discussions - a subjective account of all this will be provided. A company employee could spend 24 hours a day on all this…


Natural language processing definitely surpasses human capacity. An important feature of NLP is impartial assessment and the permanence of the result. Artificial Intelligence demonstrates an incredible ability to solve problems. And as everyone knows, anything that helps address challenges in the shortest possible time is great for business. Today, natural language processing is a common process for many people. However, only those who understand how it works and learn how to apply it can be market leaders.

Dan Irascu

Head of Marketing

Researching, analyzing, and writing insightful stuff is what I do for a long time now at Mobiteam. At TechBehemoths, I put all my experience and knowledge work for IT companies and businesses and help them reach each other.