Essay Example: Natural Language Processing in AI

Published: 2023-04-02
Essay Example: Natural Language Processing in AI
Type of paper:  Essay
Categories:  Information technologies Languages Artificial intelligence
Pages: 4
Wordcount: 984 words
9 min read

The 21st century is an era of technology, and therefore every sector is a consumer of information technology. Technology refers to a situation where scientific knowledge is applied for practical purposes. Technology is quite wide, and one of its aspects is Natural Language Processing (NLP) technology. This technology is mainly used in artificial intelligence. It is the intermediary between the natural human language such that it helps computers understand the human language. The NLP technology is empowering computers to interpret human language and respond to natural language appropriately. In other words, the main duty of NLP technology is reading, acting as a decipherer, understanding, and analyzing human language in a valuable manner (Garbade, 2018). It is important to understand that the use of natural human language, especially in writing, has become easier because of NLP technology.

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The NLP technology does several activities in the modern world. One of the duties performed by the technology is the translation of natural human language. Nowadays, computers can translate one natural human language into another through translation applications. Google translate where written content is translated into different human languages upon request. For instance, individuals may write content in English then translate to Spanish, Chinese, or any other language present in the system. The NLP technology also works in the word processors like Grammarly and MS Word software. On these platforms, NLP technology identifies and corrects the grammatical mistakes present on the typed content (Kharkovyna, 2019). They also translate of natural human language by clicking the language selection button.

The NLP technology also works on the Interactive Voice Response (IVR) applications, (Garbade, 2018). The IVR is used in business call centers, and they respond to particular users' requests. For instance, they produce receipts upon request as well as opening and closing some systems' technology also works as a facilitator of a personal assistant in various businesses. Applications such as Siri, Cortana, Google, and Alex use technology to dispense personal assistant tasks. Generally, NLP technology can play these roles because it helps computers understand human language through the user's above-mentioned computer applications.

Natural language processing technology addresses the barriers to communication. NLP has solved the challenge of human beings failing to understand each other because they communicate in a different language. For instance, it was impossible for a Chinese who has never been exposed to the English language to read and understand a message typed in English. However, due to the NLP technology, such an individual only presses the translation into the English language hence reading the content written in a foreign language. In NLP language has also solved the problem of writing essays with grammatical errors. As stated, earlier applications like Grammarly detect grammatical errors and offer corrections to the errors. Therefore, NLP technology has solved the challenge of barriers to communication and potential misunderstanding of written content because of grammatical errors.

The adoption of NLP technology is intense in especially over the last decade. Most of businesses and organizations have adopted the technology. For instance, organizations that often send many emails and letters as well as writing reports that purchase the Grammarly software (Gurulingappa et al., 2019). This enables such business personnel to produce documents and emails free from grammatical errors. Also, learners have intensely adopted the Grammarly applications; Google Translate applications are used by many business organizations, especially multinationals. Such a business has customers and employees with a range of natural human languages. Therefore, the company uses translator applications to translate customers' feedback. Generally, NLP technology is enhancing communication across the globe.

One of the challenges affecting the adoption of NLP technology is that it is difficult to develop the technology. NLP is a difficult because it integrates rules of the different natural human languages (Raguseo, 2018). Notably, different natural human languages have several rules and structures that are often difficult to understand. Therefore, it becomes harder for computer scientists to develop rules that are suitable for the passage of natural human language to computers. For example, it is difficult to develop codes that can interpret sarcasm rules, among others. This challenge is solved through the development of algorithms that puts into consideration syntax and sematic language rules. Creating algorithms that accommodate the different syntax and semantics techniques makes such an algorithm to be used for a variety of languages.

Although NLP technology has both benefits and risks, one of the benefits is efficient communication among people speaking different languages through computer applications (Pons et al.,2016). For instance, an English native speaker can send an SMS to a Japanese native speaker. The two parties have a conversation with each other without the need for a physical translator as long as they have installed the Google Translate application on their phones or computers. However, NLP technology possesses the risks of reduced physical interactions and decreased desire to learn foreign languages. Several persons do not find the need to learn foreign languages because they can still communicate with non -English people via NLP-supported programs. In other words, NLP is causing social conservatism among speaking different languages. Conclusively as the world embraces NLP benefits and should also consider the social risks posed by NLP technology.


Garbade, M. (2018). A Simple Introduction to Natural Language Processing. Retrieved 18 February 2020, from

Gurulingappa, D., Karmalkar, P., & Meegaro, G. (2019). Industry's adoption of natural language processing. Retrieved 18 February 2020, from

Kharkovyna, O. (2019). Natural Language Processing (NLP): Top 10 Applications to Know. Retrieved 18 February 2020, from

Pons, E., Braun, L. M., Hunink, M. M., & Kors, J. A. (2016). Natural language processing in radiology: a systematic review. Radiology, 279(2), 329-343

Raguseo, E. (2018). Big data technologies: An empirical investigation on their adoption, benefits, and risks for companies. International Journal of Information Management, 38(1), 187-195.

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