AI controlled design engineers are using NLP or natural language processing to communicate with robot computer programs or data to deploy code before use in industrial applications.

Natural language processing: An explainer

Posted on: October 12, 2023

In the ever-evolving world of computer science, natural language processing (NLP) is the technology that bridges the gap between human language and machine understanding, and in many ways, it has revolutionised the way people interact with computers and other machines.

It is a powerful tool, one with many applications, and its rapid growth and development shows no sign of slowing.

“Everyone knows it: advanced NLP solutions are here to stay,” says Forbes in a 2023 article. “They’ve already proven to be useful and worthy of investment.”

What is natural language processing?

Natural language processing (NLP) is the area of artificial intelligence that enables computers to understand the languages of human beings. This includes processing, analysing, interpreting, and even generating language.

There are several sub-areas of natural language processing, including:

  • Speech recognition. Speech recognition can convert spoken language into written text. Popular virtual assistants like Siri, Amazon’s Alexa, and Google Assistant heavily rely on speech-to-text software to comprehend user commands.
  • Translation. Machine translation can efficiently translate text from one language to another. Google Translate is a well-known example of NLP-based language translation, able to translate English into other languages, and vice versa.
  • Natural language generation. Natural language processing can generate human-like text based on given prompts or input. Applications include automated content generation and chatbots.
  • Sentiment analysis. Sentiment analysis works to understand the emotions behind a piece of text, and is used across social media platforms and customer feedback analysis tools to gauge public opinion and assess brand reputation.
  • Grammatical tagging. Grammatical tagging, also known as part-of-speech tagging, can determine the correct placement of a particular word within a piece of text based on its meaning and the context of the text.
  • Word sense disambiguation. Many words have more than one meaning, which is where word sense disambiguation comes in, using contextual clues to determine which option is the correct one.
  • Natural language understanding (NLU). Natural language understanding is an emerging NLP task focused on computers’ understanding of human language.

The difference between NLP and artificial intelligence

Natural language processing is an area of artificial intelligence, so while the two terms are closely connected, they are not synonymous.

Artificial intelligence refers to the wider concept of machines that are developed to exhibit human-like intelligence. They are built to learn, to solve problems, and to make decisions.

Natural language processing, on the other hand, specifically deals with human language and how computers can process, understand, and generate it. Natural language processing is one of many AI areas, sitting alongside computer vision, robotics, and more.

How does natural language processing work?

Natural language processing draws on specialisms such as computational linguistics as well as areas of artificial intelligence, including machine learning models and algorithms, and deep learning neural networks.

Typically written in programming languages Python, C++, or Java, NLP algorithms are often rule-based and are usually implemented with a few key steps:

  • Data gathering. This can include unstructured text data as well as structured data sets.
  • Preprocessing. This stage prepares data for use. It includes techniques such as tokenisation – separating text into smaller pieces – and stemming, which simplifies words.
  • Machine training. Natural language models are trained using the preprocessed data.
  • Deployment. The model is put to work in a live environment.

Natural language processing solutions may also call on other NLP technology, such as:

  • Named entity recognition as well as syntactic parsing or syntax analysis, which help machines to better understand the structure of language. 
  • Semantic analysis, which helps machines better understand the overall meaning in a piece of text.

Applications of natural language processing

The applications for natural language processing are diverse, spanning several industries and use cases. Prominent real-world examples and applications of NLP include:

  • Personal support. Many people use personal virtual assistants like Siri and Alexa for everything from getting answers to general questions, to playing music and finding out the latest weather report.
  • Customer service. NLP-powered chatbots provide real-time customer support, addressing queries and resolving issues promptly for people online. They have become commonplace in online shopping, providing quick, personalised, and predictive assistance for shoppers.
  • Healthcare. Natural language processing can be a powerful tool in the healthcare industry: according to Google, up to 80% of medical information is unstructured data.
  • Automation. Many automation platforms rely on natural language processing techniques for data labeling, information extraction, and question answering.
  • E-commerce. Online retailers often use natural language processing techniques to analyse customer reviews and feedback, and to gain valuable insights into their product preferences and sentiments. They’ll also apply sentiment analysis techniques on social media data in order to gauge public feeling towards their products and services, or make decisions around things like pricing.
  • Finance. Some financial institutions use sentiment analysis when reviewing market news and even social media data in order to make informed investment decisions.
  • Search engines. Search engines are one of the biggest users of natural language processing, applying NLP techniques to better understand user search queries and deliver more relevant search results.
  • Online safety. Natural language processing is a powerful addition to the cybersafety toolkit. Through text classification techniques, natural language processing tools can quickly detect phishing requests in emails – moving them into spam folders – or identify bots on social media platforms.
  • Data research and analysis. Natural language processing can quickly summarise lengthy documents, making it easier for people such as data scientists to get the information they need.

Explore natural language processing in greater depth

Learn more about natural language processing and the wider, high-growth field of artificial intelligence with the 100% online MSc Computer Science with Artificial Intelligence at the University of Wolverhampton. This flexible Master’s degree has been developed for forward-thinking individuals who may not have a background in computer science.

Studying this course, you will receive hands-on, cutting-edge AI training in audio-visual speech processing, as well as image and object recognition, fraud detection, deception detection, and anomaly detection. You’ll also learn about applications for big data and deep learning algorithms using popular AI and machine learning tools. Other topics of study include:

  • data science
  • machine learning
  • intelligent agents
  • data mining
  • informatics
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