Definition
Natural language processing (NLP) is a field of artificial intelligence that assists computers understand, interpret, and use human languages. NLP allows computers to communicate with people, using a human language. Further, Natural language processing gives computers the ability to read text, hear speech, and interpret it. NLP combines different disciplines, including computational linguistics and computer science, as it strives to bridge the gap between human and computer communications. Broadly speaking, NLP deconstructs language into shorter, more basic pieces, known as tokens (words, periods, etc.), and tries to understand the relationships of the tokens [1].
For instance, hospitals can use NLP-powered translation systems to help doctors communicate with patients who speak different local languages. When a doctor needs to explain "Take this medication twice daily after meals" to an Igbo-speaking patient, the NLP system translates it to "Were ogwu a ugboro abua kwa ubochi mgbe i richara nri." For Hausa patients, the same instruction becomes "Ka sha wannan magani sau biyu a rana bayan cin abinci." This medical translation technology ensures patients understand critical health instructions regardless of their primary language, reducing medication errors and improving treatment outcomes across multilingual healthcare system.
Origin
The origin of NLP can be traced back to the 1950’s, just after the invention of digital computers. NLP builds on both linguistics and AI. But, the significant advances of the past few years have been driven by machine learning, a field of AI that builds systems that learn and generalize from data. Deep learning is a kind of machine learning that can learn very complex patterns from large datasets, which means that it is ideally suited to learning the complexities of natural language from datasets sourced from the web [3].
Context and Usage
NLP is part of
our everyday lives, and its applications diverse, cutting across several
industries, making technology more accessible and user-friendly. Some of their everyday
applications include:
- Social media monitoring: NLP enables the analysis of social media content to gauge public opinion, track trends, and manage online reputation.
- Virtual assistants: Siri, Alexa, and Google Assistant are instances of how NLP are used in virtual assistants to understand and respond to user commands.
- Translation services: NLP are utilized by services such as Google Translate to provide real-time language translation, breaking down language barriers and fostering communication.
- Search engines: NLP powers search engines, enabling them to understand user queries and provide relevant results.
- Email filtering: Email services use NLP to help users manage their inboxes more effectively, such as in sorting out spam and categorizing emails.
Why it Matters
NLP research has
made generative AI possible, from large language models (LLMs) communication
skills to the ability of image generation models to understand requests. Many
are witnesses to NLP’s role in everyday life, including powering search
engines, prompting chatbots for customer service with spoken commands,
voice-operated GPS systems and question-answering digital assistants on
smartphones such as Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana.
In addition, enterprise solutions increasingly rely on NLP to streamline and automate business operations, boost employee productivity and simplify business processes [4].
In Practice
A real-life case
study of natural language processing can be seen in the case of Microsoft
Translator. Microsoft Translator is in partnership with Appen to make
synchronous multi-language communication possible across 110 languages, including
rare and endangered dialects like Maori and Basque. According to VP of Products
for Microsoft Azure AI, Marco Casalaina, “Appen’s breadth of language expertise
and ability to source speakers, even for under-resourced languages, has allowed
us to offer a wide range of languages and dialects, with Microsoft Translator.”
[5]
See Also
Related NLP and
Text Processing terms:
- Natural Language Generation (NLG): AI capability to produce human-like text or speech from data or structured input
- Natural Language Understanding (NLU): AI capability to comprehend and interpret human language meaning and intent
- Part of Speech Tagging: Process of labeling words with their grammatical categories (noun, verb, adjective, etc.)
- Text Analytics: Deriving insights from text
- Text Summarization: Condensing content automatically
References
- Keith, D. (2023). A Brief History of Natural Language Processing.
- Geeksforgeeks. (2025). Natural Language Processing (NLP) - Overview
- Eppright, C. (2021). What Is Natural Language Processing (NLP)?
- Stryker, C., Holdsworth, J. (n.d). What is NLP?
- Appen. (2024). How Microsoft and Appen Innovated AI Translation for 100+ Languages.