How Machines Understand Human Language – Natural Language Processing

We live in a time when the volume of information produced by humanity is more significant than ever before, and the amount of this data is growing every day. However, considerable benefit from this information can be obtained only with the correct processing and analysis of this data.

Every second around the world, gigabytes of new data of various types are being created: new pictures and videos are taken, hundreds of reviews are written on goods in online stores, thousands of comments appear under Facebook posts, dozens of movie reviews are created in online cinemas, stock prices are skyrocketing, then fall. And most of this raw data is practically useless. 

To get any benefit from this information, it needs to be filtered and processed. In a time when technology was not yet so advanced, all this had to be done manually. Now, many algorithms have been developed that allow you to do this using computer technology. Natural Language Processing can handle this task easily.

Artificial Intelligence Development: NLP 

Natural Language Processing (NLP) is a general direction of AI programming and mathematical linguistics. It studies the problems of computer analysis and synthesis of natural languages.

When applied to artificial intelligence, analysis means understanding a language, and synthesis means generating literate text. Solving these problems will create a more convenient form of interaction between a computer and a person.

The main areas of natural language processing include facts extraction, text sentiment analysis, answering questions, information retrieval, text generation, translation, etc. Learn more about some of them below.

Extracting Information or Facts

Information retrieval refers to the search in an unstructured or weakly structured document for specific facts of interest. For example, you have a huge number of articles in which a large number of different personalities appear, and you want to create a database that will store data about which people appearing in these articles are husband and wife. This example was used to demonstrate the capabilities of a program called DeepDive, created by a group of students and staff at Stanford University.

Sentiment Analysis of Text

The analysis of the sentiment of the text implies the automatic determination of the emotional color of the text and the identification of the attitude of the person who wrote the text to the object of discussion.

This type of analysis can be used, for example, by sellers in order to better understand which products are most popular among buyers by analyzing reviews. It can also be used by the authorities to identify the attitude of citizens of the country towards them and their decisions, etc.

The methods most commonly used in research these days are supervised machine learning methods and AI programming. The essence of such methods is that at the first stage, a machine classifier is trained on pre-marked texts, and then the resulting model is used when analyzing new documents.

Answers on Questions

This definition as a whole can fit both the chatbots, which imitate real communication with people through the transmission of text messages, and special programs that first analyze a certain text, and then answer questions related to its content. The results of one of the most recent studies on this topic are vividly described in an article by John Ball.

Text Translation

One of the most famous and frequently used areas of natural text processing in AI programming is its translation from one natural language to another. One of the most advanced techniques currently used to achieve correct translation is using neural networks like “Seq2Seq”  (sequence-to-sequence) with attention mechanisms.

Analysis Methods

To solve the mentioned above problems, researchers use a huge set of tools and techniques for natural language analysis. Some of them are highly specialized, like Seq2Seq, while others can be used in different situations, like Word22Vec.


This technology is based on the representation of words in the form of vectors of a given dimension, placing similar words close to each other. That is the distance between vectors of words denoting similar things, for example, “cat” and “dog”, will be much less than between words whose meanings have little in common, for example, “cat” and “airplane”.

This feature allows a more flexible presentation of data, which can later be used in training neural networks, various classifiers, etc. To create a base of correspondences “word – vector”, the algorithm first looks through the entire text given to it, making up a “dictionary”, which in subsequent iterations of the algorithm will be used to determine the corresponding vectors. 

Natural language texts have a large number of words that do not carry information about the given text. For example, in English, such words are articles. These words are called noise or stop words. To achieve a better quality of classification at the first stage of text preprocessing, it is usually necessary to delete such words. The second stage of text preprocessing is bringing each word to a stem that is the same for all its grammatical forms. This is necessary since words carrying the same meaning can be written in different forms. For example, the same word can occur in different declensions, have different prefixes and endings.

Neural Networks

Artificial neural networks are a system of interconnected and interacting simple processors – artificial neurons. The operating algorithm of such processors is often extremely simple. For example, the processor can simply transform the signal received at the input, using a certain mathematical function, into the output. And yet, when connected in a large enough network with controlled interactions, these individually simple processors together are capable of performing fairly complex tasks.


Recurrent neural networks differ from other types of networks in that, in addition to connections passing from one neuron to another directly, as in feedforward networks, there are also connections passing “in time”. That is, recurrent neural networks can store information over time, thereby “remembering” some data. This feature is very helpful in translating, classifying, processing natural text as a whole since our language is designed in such a way that some data at the beginning of a block of text can affect understanding and/or translation at its end.


CNN – Convolutional neural networks are best at recognizing objects and images in pictures, image classification, feature extraction, data compression. They also found an application in text processing.

NLP and Artificial Intelligence for Business

While the idea of ​​AI programming is not new, its scope has expanded significantly in recent years. More data appeared for processing, and computers not only became smarter but also began to fit in the palm of your hand. Sometimes technology reminds of itself only when it comes to high-profile novelties. But there is room for AI in big business too.

According to PwC estimates, the introduction of AI by 2030 will provide a 14% increase in global GDP (by $ 15.7 trillion). Therefore, PwC experts consider AI technologies to be the most promising direction for business development.

Final Word

CEOs around the world believe that AI will positively affect the client’s confidence in the IT industry in several years.

By successfully delivering solutions to customers, as well as developing our proprietary AI-powered platforms, FPT Software is utilizing this technology as one of the most important factors in taking advantage of the digital transformation and avoiding its potential risks. FPT Software is currently committed to implementing four core artificial intelligence technologies to develop solutions that drive our clients’ businesses:

  • NLP
  • User Behavior prediction
  • Computer Vision & Pattern Recognition
  • Sequential Data Analytics.