- 1 What is Natural Language Processing?
- 2 History of Natural Language Processing
- 3 Benefits of Natural Language Processing
- 4 Applications of natural language processing
- 5 How NLP Used in Cloud Computing?
- 6 Natural Language Processing Tools
- 7 Limitation of Natural Language Processing
- 8 Usability of NLP in AI
- 9 Conclusion
In this article, we are going to discuss the use of NLP and What is Natural Language Processing in the real world.
Here are some basic concepts we will understand What Is Natural Language Processing? and its usability, you should know before diving into this world of NLP.
Natural Language Processing (NLP) is an area in computer science, AI, and linguistics concerned with the relations between computers and human (natural) languages.
It has many applications, from search engines that use NLP to index web pages written in English or other natural languages to software that generates text-based summaries of research papers in a particular field for a general audience.
The goal of NLP is to teach computers to carry out various linguistic tasks, such as understanding human speech and producing well-formed sentences in reply.
What is Natural Language Processing?
Like many other tasks that were historically classified as artificial intelligence, NLP is a broad field. We’ll look at the specific, applied applications that involve learning and processing text.
NLP can be broken down into a few categories. Although the term artificial intelligence (AI) was popularized by researchers in the 1950s, its application within computer science dates back much further.
The term natural language processing was coined in the 1980s by C.S. Freedman and J.L. Knuth, two computer scientists, with the goal of harnessing the power of natural language processing.
NLP aims to make computers capable of understanding natural language, which is composed of words, sentences, and phrases that are composed of words.
History of Natural Language Processing
Founded in 1975, Stanford University began experimenting with natural language processing, which was still in its infancy.
In 1976, Larry Wall released his hypertext system, Eliza, which allowed users to interactively express complex ideas with just a few keystrokes.
While the late 1970s and early 1980s saw the rise of the personal computer and its use of programs such as Word, it was only a matter of time before some of these huge companies began employing natural language processing to power their own applications.
The real breakthrough, however, came in 1986 when Jean-Francois Gagne, a researcher at Bell Labs in New Jersey, discovered a way to transmit a human voice through a computer.
Benefits of Natural Language Processing
If you’re familiar with cloud computing you probably already see the benefits of natural language processing in the cloud.
The cloud is a huge part of computing today. It’s responsible for the storage, processing, and delivery of huge amounts of data. But how can it be used in NLP? What are some of the benefits?
NLP stands for natural language processing. It is one of the most important aspects of cloud computing. It analyzes human speech and assigns meaning to it. This allows people to communicate clearly with computers.
There are many benefits to using NLP, including more accurate machine translation, better customer service interactions, and smoother business negotiations. So if you want to get ahead in the world of cloud computing, learn about NLP!
Applications of natural language processing
According to ArcSoft Inc., some of the areas of applications that utilize natural language processing include:
- Business Intelligence
- Conversational commerce
- Language and customer interaction
- Power, utility, and control
- Life, health, and identity
- Demography and public information
- Supplier relations
- Pattern recognition
- Internet of things (IoT)
- Technical documentation
- Customer service
We’ve created a map below that shows the use cases of natural language processing:
How NLP Used in Cloud Computing?
The cloud is now so synonymous with the Internet that it’s easy to forget that not too long ago people were talking about “information superhighways” or “cyberspaces.” Cloud computing is a term that describes how traditional applications are being hosted and accessed over the web.
This means that instead of installing programs on your desktop, you can use web-based software to access files, websites, databases, and just about anything else.
Digital interactions have changed how we work. With more people working remotely or being self-employed, cloud computing has become crucial for getting things done.
But what is it? How does it work? And what are its advantages? Here are 3 Usage of NLP in cloud computing to make our work easier.
1. NLP Use Using REST API
you can use and implement Natural Language processing algorithms and techniques in a Cloud computing environment with the help of Rest API.
2. Language Detection in Cloud
NLP is used in a cloud environment to detect different languages from data and generate the autodetect suggestion and solution.
3. Cost Saving with Open Source
NLP libraries and tools are most of it open-source and free to implement in cloud Environments with the help of APIs. Open Source makes libraries and any usable tools fee that saves implementation cost.
Natural Language Processing Tools
While it’s certainly important to learn the fundamentals of NLP before jumping into any projects involving this technology, there are a number of available tools that can help you integrate your skills into some really creative and versatile applications.
It is one of the highly developed NLP tools to understand and evaluate human language and thing ability very extensively and help to provide the exact solution.
The first tool we want to talk about is Clear Language, which has a number of educational and professional APIs and other open-source libraries that developers can utilize to build their own applications and make use of the powerful NLP technology.
Another useful tool for NLP is Lexalytics. This platform offers software, libraries, and APIs to enable enterprise, product, product discovery, and data science teams to explore new information sources such as social media, web search, text documents, images, audio, video, and other sources.
Other Programming Libraries and Tools use for NLP
- Stanford Core NLP
- Google Cloud NLP API
- TextBlob Library
- Intel NLP Architect
Limitation of Natural Language Processing
Many technologies try to make sense of the chaotic world in which we live, including AI, IoT, blockchain, and augmented reality (AR).
None of these is without its limitations. Although they may be useful, NLP has some specific limitations that present challenges when it comes to practical use.
The biggest challenge with NLP is ensuring that a computer system can properly process and interpret spoken or written human language.
If you want a business-wide solution, you have to find a way to train your computer system to understand your customers and provide them with relevant responses.
Usability of NLP in AI
When we think of AI, many of us think of self-driving cars, automated bots, or workplace productivity.
However, the challenges of AI are not solely technological. Understanding the basics of AI is important, but mastering AI requires more than just a comprehensive understanding of algorithms. It requires an understanding of human nature.
Natural Language processing helps the AI Bots and AI machines to interpret the human language and behaviors to respond and function well.
As a computer scientist, you will recognize many of the principles and tools presented in this article.
And as an IT professional, you are surely familiar with natural language processing as an emerging technology in many computer science and technology organizations.
With natural language processing on the rise, many organizations are figuring out how to leverage it for better business outcomes.
Nitin is a professional data Engineer, Who has a Post Graduation in Data Science and Analytics and working in the healthcare sector. Experts in Data analysis, Machine learning, AI, blockchain, Data related tools, and technologies. He is the Co-founder and editor of analyticslearn.com