NLP in simple terms, or how computers learn to understand us

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Natural language processing, NLP, is an advanced technology of our time that has found application in many areas of human activity. Today, we encounter it at almost every step, from all sorts of virtual assistants like Alexa and Siri to intelligent text input, language translation, analysis of patient medical records, and more. To put it more simply, this is the technology that allows a computer to understand people and answer their questions in ordinary human language. Today, this area is still developing, and quite actively, and it may well be that in the foreseeable future, the gap between people and machines will be minimized, which will open up unlimited opportunities for interaction.

In essence, NLP is This is one of the areas of development of artificial intelligence. It is also based on machine learning and a number of related technologies aimed at processing text and natural speech. It is difficult to imagine today that the first studies of this technology were conducted back in the 50s of the last century, because it became relevant only after personal computers became widespread. Initially, it was, of course, quite primitive. Perhaps some of you still remember Clippy - an intelligent assistant in the Microsoft operating system, which periodically appeared on the screen asking if you wanted to write a letter. Another example of the original version of natural language processing, which has survived to this day - this is a wavy red line in Word, indicating that there may be a typo or a misprint in the word.

Of course, modern Natural Language Processing technology has made great strides. It took decades for it to transform into what we encounter in practice literally every day. But still, this area has been developing extremely actively, is developing and will continue to develop in the foreseeable future. It combines fairly complex computer science, linguistics and, of course, artificial intelligence. The result is a solution that can analyze and understand human language and, moreover, support natural communication with a real user.

In today's review, we will dive into the world of natural language processing. In particular, NLP: what is it and what tasks can be solved with this technology. Let's talk about how Natural Language Processing works and highlight its main elements. We will describe the stages of implementation and suggest in which areas and niches this technology has already received widespread use today. Let's highlight a number of issues that are relevant for NLP today, and also try to look into the foreseeable future to say what awaits this area soon. The information presented will allow you to get acquainted with the features of natural language processing in more detail and understand why it is needed in 2025.

What is Natural Language Processing technology?

NLP AI, that is, natural language processing, is one of the niches of artificial intelligence, where the ability for machines to recognize, comprehend and analyze text to perform speech tasks is implemented. This also includes all those actions that are related to the processing of large arrays of data expressed in natural human language. Thanks to this, the efficiency of working with texts is significantly increased.

If we talk directly about the use in SEO, NLP will be able to analyze all kinds of language patterns, capturing their context, understanding the meaning of words and even establishing the relationship between them. Thanks to this, the search engine understands the goals and intentions of the user audience much better when entering keywords, which has a positive effect on the relevance of the displayed results.

Natural language processing also has a significant impact on the formation of special functions of search engines, including thematic fragments. If we talk directly about Google, then here with the help of NLP it will be possible to extract precise information from online pages, providing short answers to user queries immediately. This is what has a positive impact on the user experience, allows you to find out the answers to questions without even going to the sites. At the same time, you can be sure that the information received will be accurate and relevant to the entered queries. If the user needs more detailed answers, they will definitely go to the sites. That is, here a positive impact on the effectiveness of attracting visitors to the site is also ensured, the visibility of the resource in search results increases.

List of tasks that NLP allows you to solve

NATURAL LANGUAGE PROCESSING AI is a technology that has quite bright prospects. Regardless of whether we say something or write, in any case we receive text. It is its processing that will be the key task facing Natural language processing. That is, absolutely all actions that can be performed with text fall under the action of this technology. Only in this case, instead of a real person, all this will be implemented using computer technologies. Today, NLP is used to solve a fairly wide range of problems:

checking grammar and spelling in texts;
performing high-quality machine translation;
voice control of various processes;
recognizing natural human speech, searching for answers and providing them in the form of text or voice recording;
studying the text, retelling its content, identifying key points, forming a kind of conclusion;
analyzing sentiments based on texts and audio;
forecasting future events based on the information provided;
classifying text content into different areas and niches;
searching for similar content, as well as displaying advertisements relevant to user queries.

Let us repeat that this technology is currently at the stage of active development. This means that this list will be systematically expanded, covering all new areas.

A little about the specifics of NLP

In order for you to understand how natural language processing technology functions in practice, you need to understand how a search engine processes queries in principle. Let's look at Google as an example, but the principle itself will be identical in any system. So, when a user enters a query in the search bar, the system initially analyzes it. This procedure is standard regardless of whether the user has entered a statement, a question, or just a set of keywords. The process of processing the specified phrase by the search engine will be carried out in the following sequence:

Initially, Google divides such a query into separate elements, taking into account their context, determining the user's intentions. At this stage, a language model is used (today it is BERT), which helps to capture the meaning of phrases as accurately as possible even when they are formulated in natural language. That is, thanks to the introduction of artificial intelligence, Google can recognize the meaning of queries with maximum accuracy almost as a real person does. As a result, processing becomes more intelligent, which has a positive effect on the quality and accuracy of the issuance.
The search engine, thanks to the use of NLP, is able to focus on specific entities present in the query. Alternatively, this can be a specific place, a specific person, an object, or even an abstract concept. Thanks to such more detailed identification, the system can determine what exactly interests a person and already provide a query that will most closely match the entered query. As a result, this will reduce the time and effort spent on finding the necessary material.
Special attention is paid to the emotional coloring. BERT is able to recognize even the tone of communication, be it neutral, positive or, on the contrary, negative. This is what allows us to interpret a person's expectations with high accuracy. Modern Google has learned to identify words such as "great", "terrible", "doubtful" and evaluate their emotional coloring. Let's assume that a phrase such as "Not everything is so clear that it was good" will be perceived by search bots as something really good, despite the positive context of this word. They will understand that the meaning here is already figurative.

We would also like to draw attention to the fact that in some cases the Google search engine classifies user queries into specialized categories and topics. This allows us to organize information and provide a truly detailed structured answer. But in any case, a detailed analysis of the user's query using natural language processing technology allows the search engine to select the most relevant pages for the user, rank them and display them on the page with the results SERP in the list of results.

The main elements of NLP

Natural language processing is based on several fairly large-scale and advanced technologies, in particular:

Computational linguistics. This is the science of how software and hardware can understand and build models of human language. Specialists working in this niche use semantic and syntactic analysis to create platforms, tools, services that will help modern machines perceive and understand human speech. Here, solutions such as translators, text-to-voice synthesizers, and specialized software designed to recognize speech are mainly used. All of this is computer linguistics.
Machine learning. This technology involves training computers based on selective information in order to improve the efficiency and effectiveness of work. Human speech has a number of unique features. It may contain sarcasm, metaphors, and various variations in the structure of the sentences themselves. It is extremely difficult for machines to grasp all of these points and take them into account in their work. We must also remember about exceptions in grammar, the presence of different semantic content in words that are pronounced and written the same way. For the most part, studying all of these nuances takes years for ordinary people, but programmers do not have that much time. Using machine learning technologies, they teach NLP applications to recognize such elements and understand what exactly the user is talking about in each specific case.
Deep learning. This is a separate area in the field of machine learning, within the framework of which computer equipment is trained in human thinking. Here, a neural network is already used, which is a set of nodes for data processing. They are structured in a special way, which ultimately allows you to build a network resembling the human brain. It is deep learning that helps computer equipment recognize, compare and classify even complex patterns characteristic of many input data.

It is the combinations of these technologies that became the basis for natural language processing. But still, the efficiency of the work largely depends on how correctly all issues related to the settings will be implemented later.

Implementing Natural Language Processing: Main Stages of Work

In most cases, the implementation of Natural Language Processing technology begins with the collection of unstructured speech and text data, as well as their preparation for subsequent model training. In particular, information from surveys, cloud storage, and emails is processed here. You can also connect all the applications that your business uses in internal processes. The work that needs to be done at this stage can be divided into 3 main stages:

Pre-processing. Specialized NLP software involves the use of such modern techniques as stemming, tokenization, lemmatization, and removal of stop words from the text. As a result, it is possible to prepare information for many other related applications. Thus, tokenization involves breaking sentences into individual words or phrases, which ultimately allows search bots to better understand the essence of the content. Stemming and lemmatization are methods that simplify words literally to their root form, select the simplest synonyms that a machine can easily understand. By removing stop words, the content gets rid of those elements that do not carry any semantic load at all, complicate sentences. Most of these are all sorts of prepositions.
Training. All the data that will be collected at the previous stage will become the basis for training natural language recognition models. As a result, NLP learns to solve certain tasks depending on what text information is provided to them. Gradually, as the system is filled with information, the training of models will become more complex, and the results of their work will become more accurate and high-quality. Practice shows that this effect can be achieved quite quickly if you make a certain effort and approach the task as efficiently as possible.
Deployment. Involves bringing the natural language processing model into use. Machine learning specialists are engaged in the implementation of such work. They deploy the model within your working network, set up automatic data acquisition. The output data available here predicts the final information with high accuracy within a particular use scenario. In practice, NLP applications are often launched directly on real data, which allows for the most accurate results to be obtained, taking into account the current situation in detail.

Let us repeat: the more attention and input data directly from practical application will be used when training the Natural language processing model, the more accurate and high-quality the results of its work will be in the future.

Main areas of NLP technology use

At the beginning of today's review, we already talked about what tasks natural language recognition technology can solve. Based on this, we can make a selection of those areas where the use of NLP will be most appropriate. In particular, today we can already highlight quite a lot of all kinds of applications that are indispensable in the daily life of a modern person. Here are just a few of the most common applications of natural language processing technology based on artificial intelligence today:

Chatbots and various virtual assistants. In particular, we have already mentioned the most popular and widespread applications here - Siri and Alexa. Here, Natural language processing is used to ensure interaction between the voice assistant and the user. In particular, we are talking about conducting a conversation in natural language.
Search engines. Modern search engines quite actively use natural language processing algorithms to ensure the deepest and most accurate understanding of user queries. This is what allows them to offer the most relevant answers, improving the user experience and, accordingly, the level of interaction with the search engine and in general. To achieve such results, bots directly analyze the context of the question and identify the main data in it. Then they select the appropriate knowledge bases, documents and extract accurate information from them, including in the form of short answers.
Language translation. If you previously used classic solutions to translate text content into a particular language, including Google Translate, then you probably noticed that the output is of dubious quality. Problems often arose where it was necessary to translate words that sound and are written the same, but have different semantic loads. Today, this problem has been completely eliminated due to the fact that artificial intelligence analyzes the context of sentences, that is, the system already understands what exactly is being discussed and performs the most correct translation. Grammar rules are also taken into account here. The result is a text that will sound quite natural and understandable even for a native speaker.
Email filtering. Surely many business representatives, and even individuals, periodically face the problem when their email box is literally overflowing with all sorts of correspondence. As a result, it turns out that really important information is lost among a huge number of empty spam mailings. You can easily avoid such a problem if you implement natural language processing technology in your work with email. In particular, you can set up various criteria based on which artificial intelligence will filter your email correspondence. Advanced spam detection technologies involve analyzing the contents of letters by scanning them and identifying words, expressions, and even intonations that indicate phishing, spam.
Finance, banking. In this niche, NLP can significantly simplify and speed up the solution of such tasks as analyzing financial reports, news information, customer reviews thanks to automation. Thanks to this, specialists will be able to literally analyze user opinions, assess risks, and identify fraudulent activities in real time. It will also be possible to offer personalized financial products or recommendations to each specific client depending on their current interests, wishes, and capabilities.
Healthcare. In this area, natural language processing allows for highly efficient monitoring of patient medical records, analyzing them, collecting various data, including clinical test results and doctors' notes. As a result, diagnosis is significantly simplified, and the likelihood of prescribing drugs prohibited by another doctor is eliminated. By identifying patterns, it is possible to make fairly accurate forecasts, identify those elements that require additional attention, including from other specialists. All this has a general positive effect on the level of patient service, improving the quality and efficiency of treatment in general.
Customer service. This is relevant for various platforms, services, companies providing certain services for online stores and more. In particular, Natural language processing allows you to connect chatbots powered by artificial intelligence to work. In this case, you get a solution that can understand user requests and respond to them in real time. All this has a positive effect on the level of customer satisfaction, and also minimizes the workload of support specialists. Real people will be involved in servicing users at higher stages of the sales funnel, while you can entrust the solution of basic issues to chatbots.
Jurisprudence. The work of specialists in this area implies the need to regularly analyze all kinds of legal documentation, contracts, and track various deadlines. All this can be quite effectively automated using NLP. As a result, the time and effort of specialists is significantly saved - it becomes possible to focus directly on strategic issues.
E-commerce. Here, artificial language processing can be used to solve many everyday tasks. Alternatively, you can entrust artificial intelligence with the selection of goods or services based on individual preferences, wishes, previous interactions with your business, and browsing history. In addition, it is also possible to analyze user responses from various sources, which will also help to understand what the audience thinks about your products, services, what they would like to improve or supplement. That is, you will have all the information that will improve the user experience as a whole, increase the quality of services and goods provided. This is what will ensure more effective business development, increase the share of satisfied customers, positive reviews, and recommendations.
Human resources. Here we are talking about automation and simplification of processing of information about clients, employees, potential applicants for a particular position. NLP will help to sort resumes and select those that meet certain criteria. It will also be possible to automate the process of collecting information about the moods of employees and potential clients in order to improve the process of interaction with them and increase the level of satisfaction.
Education. Natural language processing in this area will allow creating highly intelligent learning systems that can be easily adapted to each individual student. Thanks to this, the educational process is personalized, its efficiency and ease of interaction for the audience increase.
Analysis of user sentiment. Such works are in high demand among marketers, business representatives, and politicians. Given the fact that the amount of information is constantly increasing, all those technologies that were previously used to process them are already losing their relevance today. But at the same time, the ability to track and control public opinion is becoming an increasingly important point in achieving business success. Here, the relevance of classic questionnaires, which until recently were actively used by businesses, is already declining. That is, people simply mark the points here automatically, without even delving into the specifics of the questions. But at the same time, they independently, without any incentive or coercion, express their own opinions on social networks. NLP will be able to track all this, extract important data from the information provided.
Advertising selection. Natural language processing helps to understand the interests and preferences of the audience, which will ultimately allow you to improve your own products, launch advertising for user segments with similar needs. Also, the capabilities of artificial intelligence include searching for people who already have a strong intention to make a purchase. You can find a lot of unstructured data about the audience on the Internet. NLP will extract key information from all this, which will allow you to launch advertising as correctly as possible, getting the maximum results from it in the end. In addition, you can significantly reduce the cost per click, because the people who click on the ads are those who are already obviously interested in your products and services.

All this allows us to confidently say that modern NLP technology is capable of radically changing the entire approach to interaction with advanced products, services, software solutions. This is what makes it indispensable today in many industries.

Problems in the work of Natural language processing

Natural language processing is a technology that, along with quite significant advantages, also has problem areas. By and large, this is typical for absolutely any product, direction, process. Therefore, it is important to familiarize yourself with these points and take them into account in practice, thereby minimizing the negative impact. In particular, the following can be attributed to the disadvantages and limitations of NLP:

The need for significant computer power and time for processing. Without this, it is impossible to ensure actions with a huge array of text data and, as a result, to perform complex analysis literally in real time, which, by and large, can be called one of the key requirements of our time. But in parallel with this, we can say that the technology is actively developing, its efficiency and speed of operation are gradually increasing. This means that in the foreseeable future, all this may already become irrelevant.
Limitations in understanding the context, memory. Modern models of natural language processing are still far from perfect. In practice, there are often situations when they cannot correctly interpret or understand the content of words, especially those that have several meanings, those that are pronounced figuratively. As a result, problems with interpreting the context, incorrect analysis of the information received may be observed. That is, the problem of ambiguity and polysemy for modern NLP models remains relevant, despite the many solutions and efforts that specialists make to solve such problems.
Idioms, language features. Along with what we talked about above, there is also a similar problem. It is caused by the huge variety of languages, as well as the presence of their regional variations. We must not forget about all sorts of dialects, slang, idioms, colloquialisms. As a result, natural language processing models face problems in interpreting the material they see and the information they receive. Modern developers make a lot of efforts to prevent such a problem. They update existing models with high regularity, adding current trends and identified patterns in each new version.
The quality of the data used at the training stage, the presence of bias. The entire process of training artificial intelligence will be under the complete control of a real person. This means that subjective opinion, personal arguments, and beliefs cannot be excluded here. As a result, subsequent NLP work may lead to the emergence of biased results, which will further strengthen the existing stereotypes in society.
Lack of so-called "common sense". The essence of human thinking is that we can extract even implicit information from the text. This is facilitated by the presence of our own knowledge and common sense. But artificial intelligence does not have all this. And with a high degree of probability, it will not be possible to provide this in the foreseeable future. Despite all the opportunities that a neural network receives when processing natural language, it is still not possible to teach a machine to think like a real person. That is, NLP is not yet able to identify the subtlest nuances in the text, take them into account when generating a report, at least at the level that specialists would like to provide.
Problems with confidentiality, ethics. In parallel with how the practical use of Natural Language Processing technology is scaling, problems with compliance with basic ethical standards are increasingly arising. It is also important to remember that the information obtained during the processing of some tasks can be used by artificial intelligence in the future. And it does not matter for the system whether we are talking about something abstract or about real confidential user data. That is, the risk of potential abuse of this technology remains quite significant today. This means that we will have to make a lot of effort to ensure a high level of Responsibility for all those who use NLP in practice today.

Are all these points so critical that we should completely abandon the use of all the opportunities that natural language processing provides? Here you need to make an informed decision on your own. But with a high degree of probability, we can still say that each of you will receive much more benefits in practice. The only thing is that, as with any other work, you should be extremely careful and attentive here.

A little about the prospects of natural language processing technology

Natural language processing — This is one of those technologies that are currently undergoing rethinking. Those digital technologies that were relevant on the market relatively recently can now be called a relic of the past. But now we can confidently say that this area will actively develop, effectively integrate into other areas where artificial intelligence is also used today, affecting more and more new areas. It is already obvious that NLP is capable of radically changing the principle of search engines. So, if earlier bots gave answers from their own database, now the user is provided with relevant answers based on his previous interaction with the search engine as a whole. That is, what is guaranteed to interest a specific person is selected.

But still, you can not completely rely on this technology, as well as on artificial intelligence. The fact is that in their work today there are still many flaws that cause serious concerns. Among other things, this may include problems with compliance with current standards, norms and requirements.

And here we come to the same conclusion that we often do in our reviews devoted to artificial intelligence: high efficiency can only be achieved if all work is performed under the comprehensive control of a real person. That is, AI, as well as NLP, in particular, can become a reliable and faithful assistant, but the adoption of strategic decisions will still remain with a person and his intelligence, life experience.

Let's sum it up

If we analyze everything we talked about in today's review, we can confidently say that NLP is a truly advanced technology that opens up a lot of unique opportunities for improving content, increasing its relevance for both search engines and real users. It allows search bots to better understand the intentions and interests of the audience, thereby providing the most correct and relevant answers to their requests. Also, business representatives themselves will be able to optimize their content for search engine queries and the interests of ordinary people.

You yourself can significantly simplify and speed up the upcoming work thanks to an integrated approach. In particular, it is recommended to initially prepare content that will be simple and understandable in perception. It would be a good idea to add key queries to it, as well as entities, which will allow search engines to recognize your intentions. Alternatively, these could be brand names and individual products, geolocations and everything that will make your publications more personalized. It is also very important to pay attention to the emotional tone of the content, because this way you can strengthen trust in your company and improve audience engagement. But the text processing itself can be significantly simplified and accelerated through automation using tools that use artificial intelligence in their work.

Implementing NLP principles into your work on the Internet, including at the content preparation stage, will significantly improve the visibility of the site in search results and place on the pages those texts that users will appreciate. This means that they will not close your site immediately after visiting it, and the system will see the value of the materials presented and will increasingly offer your content in the search results. To improve upcoming work, improve its quality, simplify self-realization, take advantage of the opportunities offered by mobile proxies from the MobileProxy.Space service. At the link https://mobileproxy.space/en/user.html?buyproxy you can find out a lot of useful information about this product.

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