Natural language processing ensures that AI can understand the natural human languages we speak every day. Learn more about this impactful AI subfield.
Natural language processing (NLP) is a form of artificial intelligence (AI) that allows computers to understand human language, whether written, spoken, or even scribbled. As AI-powered devices and services become increasingly more intertwined with our daily lives and world, so does NLP's impact on ensuring a seamless human-computer experience. In a country like India, home to more than 20 regional languages, NLP can help unite more people and unite those living in different regions as the digital India movement gains popularity.
Continue reading to learn more about NLP, its techniques, and some of its benefits for consumers and businesses. You can also delve into standard NLP tools and explore some cost-effective online courses that can give you a robust introduction to the field’s fundamental concepts.
Natural language processing (NLP) is a subset of artificial intelligence, computer science, and linguistics focused on making human communication, such as speech and text, understandable to computers.
NLP has a wide variety of uses for everyday products and services. Some common uses for NLP include voice-activated digital assistants on smartphones, email-scanning programs to identify spam, and translation apps that decipher foreign languages.
NLP encompasses various techniques to analyse human language. Some techniques you will likely encounter in the field include:
Sentiment analysis: This NLP technique analyses text to identify its sentiments, such as “positive”, “negative”, or “neutral”. Businesses frequently use sentiment analysis to understand customer feedback better.
Summarisation: This NLP technique summarises a longer text to make it more manageable for time-sensitive readers. Some types of texts people summarise include reports and articles.
Keyword extraction: This NLP technique analyses a text to identify important keywords or phrases. It contributes to search engine optimisation (SEO), social media monitoring, and business intelligence.
Tokenisation: The process of breaking characters, words, or subwords into “tokens” that the program can analyse. Tokenisation undergirds everyday NLP tasks like word modelling, vocabulary building, and frequent word occurrence.
Whether you use it to quickly translate a text from one language to another or produce business insights by running sentiment analysis on hundreds of reviews, NLP provides various benefits for businesses and consumers.
Unsurprisingly, you can expect to see more of it in the coming years. Statistica projects that India’s market for NLP translation alone will grow by 25.35 percent per year [1].
Some of NLP’s benefits include:
The ability to analyse both structured and unstructured data, such as speech, text messages, and social media posts
Improving customer satisfaction and experience by identifying insights using sentiment analysis
Reducing costs by employing NLP-enabled AI to perform specific tasks, such as chatting with customers via chatbots or analysing large amounts of text data
Better understanding a target market or brand by conducting NLP analysis on relevant data like social media posts, focus group surveys, and reviews
NLP has uses in various applications, but it still has its share of challenges. Many NLP tools struggle to interpret sarcasm, emotion, slang, context, errors, and other ambiguous statements. This challenge means that you will likely need to limit NLP to unambiguous situations that don't require significant interpretation.
Although natural language processing might sound like something out of a science fiction novel, the truth is this: people already interact with countless NLP-powered devices and services every day.
Online chatbots, for example, use NLP to engage with consumers and direct them towards appropriate resources or products. While chatbots can’t answer every question customers may have, business leaders like them because they offer cost-effective ways to troubleshoot common problems or answer consumers' questions about their products.
Another standard use of NLP is for text prediction and autocorrect, which you’ve likely encountered many times before while messaging a friend or drafting a document. This technology allows texters and writers to speed up their writing process and correct common typos.
ChatGPT is a chatbot powered by AI and natural language processing that produces unusually human-like responses. Recently, it has dominated headlines because its ability to produce responses far outperforms what was previously commercially possible.
You can choose from numerous natural language processing tools and services available to help you start using NLP today. Three popular options include the following:
Google Cloud NLP API
IBM Watson
Amazon Comprehend
Python is a programming language well-suited to NLP. Some common Python libraries and toolkits for exploring NLP include NLTK, Stanford CoreNLP, and Genism.
Natural language processing helps computers understand human language in all its forms, from handwritten notes to typed snippets of text and spoken instructions. To explore the field in greater depth, consider taking a cost-effective, flexible Specialisation on Coursera.
DeepLearning.AI’s Natural Language Processing Specialisation will prepare you to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages, summarise text, and even build chatbots. In DeepLearning.AI’s Machine Learning Specialisation, you’ll master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, three-course programme by AI visionary (and Coursera co-founder) Andrew Ng.
Statistica. “Language Translation NLP - India, https://www.statista.com/outlook/tmo/artificial-intelligence/natural-language-processing/language-translation-nlp/india.” Accessed 12 May 2024.
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