By: Arya Brijith,International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, email@example.com
The cutting-edge of technical development, Natural Language Processing (NLP), facilitates computers to precisely comprehend and respond to human languages. This article explores the many facets of NLP and looks at how it might be used in fields as varied as healthcare, customer service, and education. We explore the objectives, difficulties, and bright future of NLP, providing light on its potential to fundamentally alter how we engage with technology and communicate.
NLP, artificial Intelligence, machine learning, tool, computer, health care, machine translation, question-answering, customer service, challenges.
NLP is that tool which helps the computer understand and talk back to you just like a human. It is what makes things like voice assistants, language translation, and chatbots work. So, when you ask Siri a question, or use Google Translate to talk to someone in another language, that’s NLP in action!
While texting your friend, it is noticed that your device sometimes suggests words or even complete your sentence. Same implies to chatting with a virtual assistant.
NLP also helps with things like reading handwriting on a scanned document or even figuring out if a review you read online is positive or negative. It is all about making computers understand and talk to us in a way that feels just like talking to a human.
The field of artificial intelligence known as Natural Language Processing (NLP) helps computers understand, interpret, and manipulate human language. It is the combination of linguistics and computer science. NLP uses several disciplines, such as computer science and computational linguistics, to close the gap between human communication and machine comprehension . It juggles tasks such as comprehending what we say, interpreting several languages fluently, and even reading our emotions in written words. It is like giving machines a quick lesson in human communication.
This revolution is being led by Natural Language Processing (NLP), which gives computers the capability to precisely understand, interpret, and respond to mortal language. This composition goes into the realm of NLP, examining its operation, disruptive pledge for sectors ranging from client service to healthcare, thing, challenges, and the future of NLP.
Goal of NLP
The goal of NLP is to accomplish human-like language processing[R3].
Using Natural Language Processing (NLP), it will be possible for computers to communicate with people in a way that seems more intuitive and natural. Sentiment analysis, language translation, text summarization, speech recognition, and other processes are included in this.
Ultimately, the objective of NLP is to facilitate effective communication between humans and machines, opening a wide range of applications in fields like healthcare, customer service, information retrieval, and more.
- Question-Answering: Question-answering just provides the user with the text of the answer or response-producing sections, as opposed to Information Retrieval, which answers to a user’s query by providing a list of potentially pertinent documents . NLP is essential for giving robots the ability to comprehend and react to inquiries made in human language.
- Health-care: Due to the widespread usage of Electronic Health Records (EHR), several hospitals have included NLP into their systems. . MedLEE – Medical Language Extraction and Encoding is a top-notch tool provided by NLP International Corporation to health care IT professionals. The widely used and fully tested natural language processor is relevant to a variety of medical professions, and because of its knowledge-centric architecture, it can easily adapt to different client demands. It has proven to be exceptionally skilled in coding for UMLS, RxNorm, and SNOMED data. The design of MedLEE offers both our cherished clients and our collaborative partners considerable advantages in terms of speed, accuracy, and cost-effectiveness. MedLEE search is a semantic search, retrieval, and real-time analytics module that significantly increases users’ ability to utilise data processed by MedLEET, an NLP engine developed by Columbia University and only licensed to NLPI for commercialization. Numerous NLP systems have been used for radiology report classification, with an accuracy often like that of humans.
- Customer service: Chatbots that use natural language processing (NLP) can respond quickly to client concerns and issues around the clock. To determine customer satisfaction levels, it may evaluate feedback. This enables companies to pinpoint problem areas and swiftly fix them. Furthermore, NLP may suggest goods and services that match client tastes and behaviour, improving the whole purchasing experience.
- Machine Translation (MT): The statistical machine literacy collects as much information as it can that seems to be similar across two languages, then crunches its data to determine if a given good in Language A is equivalent to a given good in Language B. As for Google, it revealed a new machine translation system based on Deep Learning and artificial neural networks in September 2016 . The usage of NLP by these systems, which range from ‘word-based’ approaches to applications involving deeper levels of analysis, makes MT systems perhaps the first instance of NLP.
- Education: With features like pronunciation feedback, language translation, and contextual language learning tasks, NLP-powered products can help with language training. In order to provide individualized learning materials and tasks, it may also examine students’ learning styles, aptitudes, and deficiencies. By doing this, the curriculum is modified to meet the requirements of each student.
Long instructional materials can be summarized, making complicated topics more approachable. Text-to-speech features can assist kids who have trouble reading by providing them with these tools.
Virtual tutors or chatbots powered by NLP may help students with their assignments, explain ideas, and give step-by-step instructions.
It is challenging to create a program that can comprehend natural language. The great majority of natural languages have an endless number of sentences. 
The system may have difficulty in tackling homophones. Examples of homophones include- “write” and “right,” “plane” and “plain,” etc.
Language includes common phrases, idioms, and cultural jargon. Making computer software that can understand natural language is difficult. The models may be enhanced if they had access to a lot of data to train and update often. Dealing with words that have distinct meanings in various geographic contexts is a very challenging problem .
NLP models like Chat GPT and Google Bard provide a broad range of potential, but there are also a few difficulties (or ethical issues) that need to be resolved. The first difficulty is that of precision. The system’s accuracy is greatly influenced by the standard, variety, and complexity of the training data as well as the standard of the input data supplied by the students.
NLP has enormous potential since it combines computer science and linguistics. The goal of this discipline is to teach robots the complex skill of human discourse. The possibilities are endless, from linguistic fluency to written emotional comprehension.
The importance of language in the digital era goes well beyond just communication. It is the key to revealing thoughts, feelings, and unnoticed patterns in large amounts of text. This movement is being led by NLP, which gives computers the ability to precisely comprehend, decipher, and respond to human language.
It is a journey that is changing how we engage with others, learn, and communicate. NLP is a magnificent stroke of genius in the big fabric of AI.
The following time you find yourself in awe of a virtual assistant’s fluency or the accuracy of language translation, just keep in mind that NLP is what makes it all possible. Embrace this linguistic revolution because it holds greater potential for the future.
- Prakash M Nadkarni, Lucila Ohno-Machado, Wendy W Chapman, Natural language processing: an introduction, Journal of the American Medical Informatics Association, Volume 18, Issue 5, September 2011, Pages 544–551, https://doi.org/10.1136/amiajnl-2011-000464
- Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649.
- Liddy, E. D. (2001). Natural language processing.
- Fanni, S.C., Febi, M., Aghakhanyan, G., Neri, E. (2023). Natural Language Processing. In: Klontzas, M.E., Fanni, S.C., Neri, E. (eds) Introduction to Artificial Intelligence. Imaging Informatics for Healthcare Professionals. Springer, Cham. https://doi.org/10.1007/978-3-031-25928-9_5
- Cai, T., Giannopoulos, A. A., Yu, S., Kelil, T., Ripley, B., Kumamaru, K. K., … & Mitsouras, D. (2016). Natural language processing technologies in radiology research and clinical applications. Radiographics, 36(1), 176-191.
- Zajac, R., Casper, M., & Sharples, N. (1997, March). An open distributed architecture for reuse and integration of heterogeneous NLP components. In Fifth Conference on Applied Natural Language Processing (pp. 245-252).
- Fuchs, K. (2023, May). Exploring the opportunities and challenges of NLP models in higher education: is Chat GPT a blessing or a curse?. In Frontiers in Education (Vol. 8, p. 1166682). Frontiers.
- Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia tools and applications, 82(3), 3713-3744.
Brijith A. (2023) Natural Language Processing (NLP): Harnessing its Potential, Insights2Techinfo, pp.1