Exploring Natural Language Processing Techniques

Written by Coursera Staff • Updated on

Natural language processing techniques like tokenization, part-of-speech tagging, and syntax analysis make it possible for NLP applications to complete many helpful tasks. Explore NLP techniques and natural language processing applications capabilities.

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Natural language processing (NLP) is an artificial intelligence technique that combines computational linguistics with deep learning to understand natural human languages, such as English, Mandarin, or Swahili. As a form of artificial intelligence, NLP allows a computer to analyze text and speech using a variety of techniques to understand a natural language and respond accordingly.

Researchers and computer scientists have been thinking about and working on natural language processing for as long as computers have been around. Fortunately, advances in statistical models, deep learning, and pre-trained language models are making natural language processing techniques and tasks more relevant and popular in areas like linguistics, cybersecurity, and even fields you might not expect, like gender studies, dentistry, and quantum mechanics.

Something to think about...

If you enjoy learning about aspects of artificial intelligence, you could consider a career in the NLP industry. Statista estimates that the market size for NLP will increase from 12.88 billion dollars in 2025 to 41.79 billion dollars by 2030, an annual growth rate of 27.54 percent [1]. This data suggests robust growth that can benefit from professionals with the skills and interest needed to work in the field. 

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Explore how organizations and companies use natural language processing techniques like supervised and unsupervised learning to perform machine translation, sentiment analysis, information retrieval, and more. 

What is natural language processing? 

Natural language processing is a technology that allows computers, machines, and other artificial intelligence models to understand, process, and respond to natural human language. A natural language develops naturally, like any language you use to communicate with another person. These languages are natural instead of languages like Python, C++, or HTML, which programmers use to communicate with computers. Using computational techniques like text preprocessing, feature extraction, and text analysis, computers can use NLP to automate tasks, analyze customer sentiments, provide virtual customer service, and accomplish more tasks. 

Why is natural language processing important? 

Natural language processing is important because it changes how you can interact with computers and machines and allows robots and artificial intelligence to work in new ways. For example, early forms of search engines only indexed pages by topic. When you wanted to search for a topic, you would type the keyword into the search engine and return a list of pages indexed to that topic.

Natural language processing techniques have evolved over time to allow search engines a better understanding of what you really want to find when you type a query into the website. Today, NLP can understand you so well that you don’t have to type anything into a search engine to look for information; you can simply ask your voice assistant—Google Assistant, Siri, or Alexa—a question, which will respond in kind. 

From voice search to 24-hour customer service chatbots to content moderation, you are likely already benefiting from advances in NLP technology. The more scientists and researchers improve and adapt natural language processing, the more companies and organizations can harness its power for task automation, data analysis, information retrieval, content generation, and more. 

How does natural language processing work? 

Natural language processing works by understanding and analyzing text through several computational processes, including tokenization, stemming, stop word removal, feature extraction, part-of-speech tagging, sentiment analysis, and named entity recognition. 

  • Tokenization: The AI algorithm breaks down text into words or phrases and in some instances, represents these fragments as numerical expressions. 

  • Stemming: The model reduces words to their root form to make it easier to work with languages by grouping similar words together. For example, you can reduce “eating,” “eats,” and “ate” to the word “eat”. 

  • Stop word removal: The NLP model filters out words like “is” and “the,” which are important for understanding natural language, but they don’t add significant meaning to the text. 

  • Feature extraction: In terms of NLP, feature extraction is the process that transforms raw text into numerical data that computers can analyze and comprehend.

  • Part-of-speech tagging: The algorithm analyzes text to tag each word or phrase—each token—as what part of speech it is, such as a noun, verb, or adjective. 

  • Named entity recognition (NER): This process allows NLP algorithms to identify named entities or items with proper names, such as The Mona Lisa, Betty White, or New York. 

  • Sentiment analysis: AI models can use sentiment analysis to understand the emotions and feelings people use when typing or speaking, labeling the text as either positive, negative, or neutral. 

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Natural language processing techniques

You can also use various other techniques to customize natural language processing applications for many different purposes; some of these techniques include supervised, unsupervised, and semi-supervised learning, syntax and semantic analysis, and rules-based, statistical, or deep learning NLP. 

Supervised, unsupervised, and semi-supervised learning

These natural language processing techniques describe how much of the algorithm’s training data you label. Essentially, in terms of NLP, supervised learning utilizes data that is labeled, while unsupervised learning uses data that is not. AI models require training data to help them analyze text by comparing new input to that training data, which can be labeled (supervised) or unlabeled (unsupervised).

  • In supervised learning, you will label training data and provide the algorithm with more information about what you want it to do with new inputs.

  • In unsupervised learning, the statistical language model provides those labels by analyzing what the most likely label should be, predicting patterns, and adjusting its estimates with new information.

  • Semi-supervised learning is a combination of techniques that can help provide specific instructions to the algorithm while saving time spent training the model. In this type of machine learning, you will use a training data set that typically contains a small amount of labeled data and a large portion of unlabelled data. 

Syntax and semantic analysis

Natural language processing applications use syntax and semantic analysis to determine the meaning of individual words and how words change meaning based on where they appear in a sentence. Syntax analysis deconstructs a sentence into its basic grammatical components to assist in comprehension, while semantic analysis helps NLP models understand what individual words mean when placed in a sentence. These types of analysis are especially critical in machine translation where one language's semantics differ from another's. For example, when translating between English and Spanish, you would change some word order: “The red car” would become “el auto rojo,” or “the car red.”

Rules-based, statistical, or deep-learning NLP

Over time, computer scientists and researchers have developed three main frameworks for natural language processing models: Rules-based, statistical, or deep learning. Rules-based frameworks were some of the first to develop and required developers to designate a series of preprogrammed rules for the natural language model to follow, such as segmenting sentences by punctuation. As a programmer, you would typically add the rules manually, and the model would offer limited functionality without the need for machine learning.

Next came statistical models, which used statistical probabilities to analyze, classify, and map text to determine meaning. The technology requires machine learning because the algorithmic model identifies patterns and uses training data to understand and interact with language. 

Deep learning models represent the most recent advance in natural language processing applications and can process much larger amounts of data, allowing for increased accuracy and functionality. You can now access several different pre-trained language models, such as BERT and GPT, to adapt your own natural language processing application without starting from scratch. 

Natural language processing tasks

Altogether, these natural language processing techniques power a wide menu of tasks NLP applications can perform. Consider how you can use NLP for natural language generation, machine translation, and more: 

  • Natural language generation: AI models like GPT-4 can use natural language processing to generate text responding to a prompt, such as writing a product description, essay, or creative writing. 

  • Machine translation: NLP can empower a machine to translate from one natural language to another by understanding and applying each language's grammatical rules. 

  • Information retrieval and semantic search: You can use NLP to find information within a vast database, similar to how a search engine combs the internet to answer your query. NLP allows you to use this technology in your local files or network to ask your NLP-controlled search engine to track down certain information.

  • Speech recognition: NLP models can transcribe verbal language to written text and are commonly used for automated customer service and dictation software. 

  • Sentiment analysis: Companies can use NLP to conduct sentiment analysis, also called opinion mining, to understand how people discuss their brands or products on social media and other online places. 

  • Content moderation: One form of sentiment analysis is content moderation, where websites with a lot of user-generated content, such as forums or social media, can monitor the types of content and sentiments that users express online. This allows filtering out hateful, violent, or otherwise malicious content in community forums. 

Continue exploring natural language processing techniques on Coursera

Natural language processing allows computers to understand, analyze, and respond in a natural language that’s easier for humans to understand, allowing AI to accomplish various tasks.

To learn more about natural language processing techniques, check out some of the courses on Coursera. For example, you could consider Natural Language Processing in Microsoft Azure offered by Microsoft. You could also complete a series of classes and earn a specialization, like the Natural Language Processing Specialization or Deep Learning Specialization, both offered by DeepLearning.AI.

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Article sources

  1. Statista. “Natural Language Processing - United States, https://www.statista.com/outlook/tmo/artificial-intelligence/natural-language-processing/united-states.” Accessed February 3, 2025.

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