What is natural language processing and its practical applications (simplified explanation)

What is natural language processing and its practical applications (simplified explanation

Language has been the basis of human communication for thousands of years and every development related to it is considered a milestone in the history of human societies.

The discovery of writing more than five thousand years ago helped sow the seeds of the civilizations we live in today.

 This is why giving computers and machines the ability to understand and generate language as we humans do will undoubtedly be a defining point for our future.

I can't say we succeeded, but we came very close thanks to the rapid development of natural language processing.

 

What is NLP Natural Language Processing?

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

Natural language processing is also one of the most important and difficult areas of AI at the same time.

it is crucial for the improvement of artificial intelligence devices and machines, but it also presents many challenges which we will discuss later in this article.

Natural language processing is not a modern science, but its theoretical origins go back hundreds of years.

and the beginning of its actual existence dates back to 60 years from now, and seeks to integrate computational linguistic algorithms in addition to statistical machine learning and deep learning algorithms, in order to make the machine capable of understanding language and its complex meanings .

 This technology is used in many things we use every day, from search engines, writing systems, debugging in cell phone whiteboards, and even medical, financial, and academic research, and we'll talk about the many uses of NLP.

 What is meant by natural language in NLP are human languages ​​such as Arabic and English that originated and developed without pre-established planning or rules.

and they have many different local dialects and colloquial dialects, and they also develop automatically, and therefore require an update in extracting their rules as time goes by.

 As for artificial language, it's like programming languages ​​like Python and others, whose rules and terminology are set by humans, and are usually not used between humans and each other, but rather between humans and computers because they are clear and direct.

 and contain no ambiguity or linguistic ambiguity or the possibility of another meaning at its beck and call.

 Read also: Learn Artificial Intelligence (all the resources and information you need)

History of NLP Natural Language Processing

 It is difficult to tell the story of natural language processing, because the science of NLP is as old as linguistic thought and philosophies, so we will try to summarize it in a few points for simplicity:

The 17th century AD:

 There were huge philosophical efforts in the seventeenth century AD to develop linguistic mathematical models, and among the most famous philosophers who made efforts in this field were Descartes and Leibniz, and many philosophers, psychologists and mathematicians succeeded in studying language from different aspects.

 The 30s:

 At the height of the Industrial Revolution and the beginning of the modern inventions we know today, researchers were encouraged to try to invent a machine capable of translating speech between French and English, but this attempt did not pay off.

In the year 1950 AD:

 Prominent scientist and father of artificial intelligence Alan Turing introduced the Turing test to the world, which states; If the result of the AI ​​model cannot be distinguished from what is produced by human beings, then in this case this model will be highly efficient, and it is the law that is heavily used in NLP natural language processing.

 In the year 1954 AD:

 The beginning of the famous "Georgetown" experiment was with IBM, which automatically translated 60 sentences from Russian to English and vice versa, which is a major breakthrough in the science of natural language processing.

 In the 60s:

Many linguists, led by Noam Chomsky, have achieved great achievements in the field of language theory and linguistic sciences.

In the year 1968 AD:

 MIT launched Terry Weingrad's "SHRDLU," which was one of the first programs to have a real, artistic, and meaningful dialogue with a machine.

In the year 1991 AD:

 With the beginning of the diffusion of computers, the “Dr. Sbaitso” which was an artificial intelligence that simulated a human psychotherapist on the DOS operating system.

 In the year 2006 AD:

 The famous Watson system was launched by IBM, which used very powerful algorithms trained on large amounts of data.

 In the second decade of the twenty-first century:

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

Uses and applications of NLP natural language processing

 Natural Language Processing NLP is a very large science, and therefore has hundreds of important uses and applications.

and although we have covered a few of them when discussing the different techniques of this science that are direct uses of NLP, there are many others.

 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, daily routine tasks, customer service and many others.

 Customer feedback analysis

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

 Through this, you can conduct an in-depth survey of the opinions of a large number of customers at a lower cost and in a much shorter time, and this is something much needed especially if your company serves millions of customers.

 Automation of customer service activities

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

 Chatbot

 Chatbots are one of the most famous natural language processing applications, where through intelligent algorithms you can easily provide artificial intelligence that answers your customers intelligent human answers, and you can also provide expert systems that can answer all your customers' questions.

 There are currently many popular applications that use these algorithms, the most prominent of which are Apple's Siri virtual assistant systems and Google's Google Assistant.

 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, we can also analyze these records and automatically extract the information we want.

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

Text prediction

 This app we use many times a day with text prediction and autocorrection which are the two features we have on our mobile phones, and it is also widely used in search engines.

 Proof-reading

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

 Email filters

 This app helps to classify Gmail email into essential messages, social messages, promotional messages and identify spam messages.

 Plagiarism detection

 The issue of plagiarism is very important in the academic community, as it compares the texts written by the researchers with those written by the broadcast to determine the percentage of citation in their texts and research.

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