What is Natural Language Processing (NLP) and its practical applications (simplified explanation)

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Language has been the basis of human communication for thousands of years, and any development that follows it is a milestone in the history of human societies. The discovery of writing more than five thousand years ago contributed to laying the seeds of the civilizations in which we live today.

That's why giving computers and machines the ability to understand and generate language as we humans do will undoubtedly be a watershed in our future, and I can't say we've done that, but we've come very close through the rapid development of Natural Language Processing.

What is NLP Natural Language Processing?

 Natural Language Processing, or NLP for short, is the science that combines language and a number of areas of computer science, such as: Machine Learning, Deep Learning, Artificial Neural Networks.

Natural language processing attempts to make the machine capable of understanding and generating human language, whether written language or audible language.

Natural language processing is also an area of ​​more

 Artificial Intelligence is important and difficult at the same time, as it is fundamental to improving artificial intelligence devices and machines, but it also has many challenges that we will discuss later in this article.

Natural language processing is not a modern science, but rather its theoretical origins go back hundreds of years, and the beginning of its actual existence dates back to 60 years from today, and it is trying to integrate computational linguistics algorithms in addition to statistical algorithms for machine learning and deep learning, in order to make the machine able to understand language and its complex meanings. .

This technology is used in many things we use every day, from search engines, typing systems, and debugging in mobile keyboards, to medical research, business and academic research, and we'll talk about the many uses of NLP.

Natural language in NLP means human languages ​​such as English that originated and developed without planning or pre-established rules, have many different local dialects and colloquial dialects, and evolve automatically, and therefore require an update in their grammar extraction over time.

As for the Artificial Language, it is like programming languages ​​such as Python and others, in which humans set their rules and terms, and they are usually not used between humans and each other, but between humans and computers because they are clear and direct, and do not contain any ambiguity or linguistic confusion or the possibility of having another meaning for its orders.

Also Read: What is Artificial Intelligence (All the Resources and Information You Need)

History of Natural Language Processing NLP

It is difficult to tell the history of natural language processing, and this is because the science of NLP is as old as thinking and linguistic philosophies, so we will try to summarize it in a number of points for the sake of simplicity:

1. The seventeenth century AD:

There were tremendous philosophical efforts in the seventeenth century AD in order to develop linguistic mathematical models, and one of the most famous philosophers who made efforts in this field was: Descartes and Leibniz, and many philosophers, psychologists and mathematicians followed them trying to study language from different aspects.

2. The 1930s:

At the height of the Industrial Revolution and the beginning of modern inventions that we know today, researchers were encouraged to try to find a machine that could translate speech between French and English, but this attempt did not bear the desired results.

3. In the year 1950 AD:

The famous scientist and father of artificial intelligence Alan Turing presented the world with the Turing Test, which states: If the result of an AI model is indistinguishable from what a human produces, then this model is very efficient, a law that is often used in NLP.

4. In the year 1954 AD:

It was the beginning of the famous “Georgetown” experiment with IBM, which was an automatic machine translation of 60 sentences from Russian into English and back, which is a very big breakthrough in the science of natural language processing.

5. In the 1960s:

Many linguists, led by Noam Chomsky, have made great achievements in the field of language theory and in linguistics.

6. In 1968:

MIT launched "SHRDLU" by Terry Weingrad, which was one of the first to do real, tactful, and sensible dialogue with a machine.

7. In the year 1991 AD:

With the beginning of the spread of computers, the “Dr. Sbaitso” was an artificial intelligence that simulated a human psychoprocessor on DOS.

8. In the year 2006 AD:

IBM's famous Watson system was launched, which used extremely powerful algorithms and rehearsed large amounts of data.

9. In the second decade of the twenty-first century:

This decade was the beginning of the launch of virtual assistant systems, such as: Siri from Apple, Alexa from Amazon, Google Assistant, and others.

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Types of NLP

There are dozens of types and Techniques of Natural Language Processing that have hundreds of uses and applications that make our lives easier every day.

Here's an overview of the two major types of NLP

In this article, we will discuss the most important techniques of natural language processing, but before that we need to clarify that these types and techniques can be divided into two major headings, namely:

 Natural Language Understanding or NLU

This part is about dealing with and understanding the natural language after it is presented as input to the computer or the machine, whether this language is presented in the form of text or sound, the goal here is to make the machine understand the natural language.

Natural Language Generation or NLG

Natural language generation is the technology that allows the machine to be able to generate content, whether textual or audio, similar to what a normal human might generate, meaning that in this technology the machine deals with language as output, not input.

Multiple Techniques for Natural Language Processing

1) Speech recognition

This technology is widely used these days, and is used to convert voice data into text data, with the aim of giving voice commands or asking some questions in a voice without having to type.

This technology faces many challenges, such as: multiple dialects, incorrect pronunciation, similar words, linguistic errors, and many more, but despite this we are witnessing a remarkable development in its applications such as Alexa and Siri.

Speech recognition technology may be used in virtual assistance systems, customer service and complaint response, and dozens of other uses.

2) Text Classification

Text classification is one of the most widely used and popular natural language processing techniques in our time, this technology has been developed since the beginnings of NLP, besides this it is the most widespread and used in our daily life, for example in classifying our emails.

Through this technology, we can deal with texts of various forms and purposes, and then distribute them into different categories based on their content. Among the most prominent sub-technologies of Text Classification:

a) Topic Classification

Using this technique, the various texts are processed and then their main topics are identified, and then arranged according to their own topics and placed in the appropriate categories.

This technique helps when dealing with many texts that are difficult for a person to read, and put them in specific categories, whether because of their difficulty or for taking a great deal of time and effort.

Many companies also use this technique in managing their customer problems, as they pass the customer complaint on a workbook to put it in specific categories according to the texts inside.

b) Intent Detection

In this technology, artificial intelligence algorithms identify the goals, objectives, and intent behind texts or speech, and so this technology helps many companies improve and manage departments such as customer service or sales within them.

c) Authorship Attribution

This technique is very interesting as it deals with creative works that raise controversy about their authors. By feeding the algorithm with the different works of the questionable authors, and then feeding it with the work in question, it can attribute the work to its real author.

3) Text Extraction

Another important NLP technology is Text Extraction, a technology that helps save a lot of time and effort when you need to search for information or specific pieces of text within very large and wide books or content.

There are several important algorithms and techniques that fall under the major text extraction technology:

a) Keyword Extraction

This technology can automatically extract the most important keywords and expressions in a text or group of texts, and this technology is widely used in Search Engines.

b) Named Entity Recognition or NER

This technology is very important as it makes the machine able to respond better to natural language, as it can extract words that have other meanings, such as being the name of a famous person, the name of a movie or series, or even the name of a place.

4) Machine Translation

As we know from our talk about the history of Natural Language Processing, machine translation was one of the first problems that NLP tried to solve, which despite having reached a great degree of professionalism and accuracy like that in Google Translate, there is a lot ahead for us to reach the accuracy of translation Humanity.

5) Sentiment Analysis

 Sentiment Analysis is one of the most famous NLP techniques that has caught the attention of the world in recent times. This technique attempts to understand the feelings that lie behind texts and identify the situations resulting from them, whether they are specific feelings, confusion, irony, doubt, or something else.

Today, many companies use this technology in order to automatically respond to their customers, as they can distinguish the complaint from praise and respond to the two in appropriate ways according to the orders specified by the company.

6) Automatic Text Summarization

Automating summarization has been a priority for natural language processing professionals for decades, because this technology can save us humans a lot of time and effort that we spend in performing this difficult task.

Now researchers can summarize dozens of scientific research in various disciplines without making a great deal of effort in treating and dealing with them, which helped save them a great deal of time in performing their own experiments and research.

7) Part of speech tagging

When dealing with language or texts, machines need to identify or classify the parts of speech, which is done through part of speech tagging, as the sentence is divided into its parts of verbs, adjectives, nouns, adverbs, etc., and this helps the machine to correctly understand the text or sound that it sends her human.

8) Word Sense Disambiguation or WSD

Some words have many meanings and even have many grammatical forms (noun, verb, ....) which can cause confusion for the machine, and requires the use of specific algorithms in order to address this problem.

Word Sense Disambiguation or WSD technology deals with the context of the sentence, and through it you can determine the intended and intended meaning of this word, and this is done through complex statistical models with weighting of words with the highest probability.

It can be said that this technology is what enables the machine to get a sense of the meaning of words and the distinction between them, that is, it is one of the most important techniques that make the machine understand natural language.

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Uses and applications of NLP

The science of NLP is a very large science, so it has hundreds of important uses and applications, and although we covered some of them while talking about the different techniques of this science that are direct uses of NLP, there are many more.

The science of Natural Language Processing has greatly helped us to perform the tasks required of us efficiently and effectively, and today it is an essential component of many applications such as Search Engines, performance of Daily Routine Tasks, Customer Service, and many others.

1. Customer Feedback Analysis

Many companies use Natural Language Processing (NLP) to analyze the opinions of their customers on social media platforms, especially Twitter, through specialized algorithms to know what customers think about your products, the improvements they want and all their comments.

Through this, you can conduct an extensive survey of the opinions of a large number of customers at a lower cost and in a much less time, and this is something very necessary, especially if your company serves millions of customers.

2. Automation of Customer Support Tasks

On average, customer service departments face hundreds of requests and complaints every day, and these are usually repetitive things, so some companies resort to automating repetitive tasks and reducing the number of workers in customer service departments, which saves them a lot of money and increases the efficiency of the department.

3. Chatbots

 Chatbots is one of the most popular natural language processing applications, where through smart algorithms you can easily provide artificial intelligence that responds to your customers intelligent human responses, and you can provide expert systems capable of answering all your customers' questions.

There are a lot of popular applications currently that use these algorithms, most notably the virtual assistant systems such as Apple's Siri and Google's Google Assistant.

4. Analysis and Categorization of Medical Records

Thanks to the development of NLP technologies, we can classify and review medical records without any human intervention through simple algorithms, and we can analyze these records and extract the information we want automatically.

This application will make us able to understand diseases and patients in a more advanced way, and it will also make us able to control and prevent the spread of infectious diseases.

5. Text Prediction

This app we use many times a day with text prediction and auto-correction which are the features on our mobile phones and it is also frequently used in search engines.

6. Proofreading

Many text programs, such as Microsoft Word, use natural language processing techniques to review and correct grammatical errors, and there are applications that only specialize in this matter, such as Grammarly.

7. Email Filters

This application that helps classify Gmail email into primary messages, social messages, promotional messages as well as identifying spam messages.

8. Plagiarism Detection

 Plagiarism is very important in academia, as it compares texts written by researchers with those written by copying to determine the percentage of citation in their texts and research.

Because it is difficult to do this manually, applications that use NLP have helped the scientific community a lot in this regard.

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Challenges to NLP

Natural languages ​​are very difficult even for us humans, even if they are from the same narrow geographic range, speak the same dialect, and are the same age, let alone train a machine or artificial intelligence to do this difficult job and to speak as well as a human.

There are many difficulties and challenges facing specialists in natural language processing, which make us so far we have not reached the hoped-for development that we seek, and these challenges are:

1. Precision

We cannot say that our natural language processing algorithms are very accurate, at least when we talk about the common ones and not the advanced ones that are monopolized by the big technology companies, so the human language processing ability still often wins over artificial intelligence algorithms.

Natural language is not as straightforward as the artificial language understood by a machine. Rather, it is complex and ambiguous and contains many variables and ambiguities that make the task very difficult for computers.

2. Dialects and slang

Natural languages ​​contain many dialects, our Arabic language, for example, is divided into dozens or even hundreds of local dialects, and even these major dialects are divided into other dialects This multiplicity and diversity is very ambiguous and difficult for a machine, so scientists today focus on the standard languages, but there are high hopes in the future that machines will be programmed to understand standard and colloquial human languages.

3. Crash Blossoms Problems

Natural languages ​​are characterized by ambiguity and confusion even in the light of the use of spelling and grammatical rules, let alone when they are not used or when they are used incorrectly. Therefore, there are many cases in which the machine does not understand the intended meaning of the speech or misunderstand it.

For example, the English sentence: A woman without her man is nothing. If you present it to the machine, it will suffer some kind of defect, or at least make an inaccurate choice. This sentence can be understood in two ways:

The first picture: A woman: without her, man is nothing, meaning that a man without a woman is nothing.

The second picture: A woman, without her man, is nothing, meaning that a woman without her man is worth nothing.

So how will the machine understand this sentence?

These ambiguous or ambiguous sentences are repeated so often that they are called “Crash Blossoms.”

4. Metaphors

We as humans use metaphors a lot in our daily conversations, which we humans can understand, deal with, and respond to them with appropriate responses, but machines cannot do this as easily as we do, so specialists have to make fantastic efforts to make the computer can understand these metaphors as we understand them.

For example, we often say to an indifferent or provoked person, “You are cold,” which the computer will understand as an indication that this person has a low temperature and that he is physically cold, and this is of course not the intended meaning.

5. New words

Most languages ​​allow us the ability to create new words with understandable meanings through a number of linguistic and grammatical tools. For example, we can use the root word through a known morphological weight to create a new word, or use suffixes and prefixes to create a new word that can be understood by the person who He hears it for the first time.

But the computer will have very big problems dealing with it and understanding it first, and then responding to it and responding to it with appropriate responses.

6. Keywords

There are many words that we use, which may have a linguistic meaning other than the common meaning in which we use them, and these words are usually the names of personalities, names of countries and geographical areas, names of geographical events, and many other things.

For example, in English, names of people, such as Goodman, may be treated by a computer as a semantic description, making their results and responses inaccurate.

7. The purpose of the talk

One word in the language may be used several uses, and this depends on the tone of the individual and the situations in which it was said and the method, which may cause a 180-degree change in the meanings of these words and thus in the appropriate responses to them.

8. Joking

Computers and machines don't have the same wonderful ability as our own to joke around and use words, expressions, or even similar words for a joke, which makes them less efficient at conducting real conversations with humans.

9. Language development

The final challenge that must be addressed is that language is constantly evolving and changing, and the machine must keep pace with this great change if it is to properly understand and respond to humans.

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