Natural language processing

Natural language processing (NLP) is a technology that combines the power of artificial intelligence (AI) and machine learning with linguistic theories. It enables computers to work with language, including speech, text and more. NLP is a powerful tool that can be applied to a wide range of systems.

What is natural language processing?

Natural language processing definition: Natural language processing is a subfield of AI that enables computers to understand, process, categorize and generate human language, whether written or spoken.

Natural language processing is an interdisciplinary technology that brings together theories of linguistics with computer science. It harnesses the power of machine learning to train computers to understand human language, allowing computer systems to process, categorize and even generate linguistic text.

More powerful than simply being able to interpret language at face value, the core of NLP is the ability to process language in the way it is spoken and written. This capability powers computer systems and artificial intelligence tools to generate text, providing the basis for generative AI.

How does natural language processing work?

Natural language processing combines several high-level technological systems, including computational linguistics, machine learning and deep learning.

As with many machine learning models, natural language processing begins with a training phase. NLP models are trained on examples of human language, such as words and phrases. Crucial to this stage is providing context, so that the NLP model can understand language not only in individual words or phrases but also in full sentences, in ways people typically speak.

Computational linguistics is fundamental to this technology, which applies computer science to the task of understanding language. It involves developing algorithms and processes for working with language, including understanding linguistic rules, formatting, grammar, semantic frameworks and tonal context.

Once NLP models have been trained on examples of written and spoken language, they can be used to process large sets of text and speech. Common use cases for natural language processing include processing large volumes of textual content quickly and efficiently, with more precision than a human. For businesses, this use could involve sorting through recordings of speech to transcribe, notate, summarize and categorize information.

What is the history of natural language processing?

Although natural language processing is a form of computational linguistics, the linguistic and technological theories that form its foundation have been evolving for many decades.

• 1900s, early origins: In the early 20th century, linguistics scholars theorized a foundation for natural language processing by offering systematic frames of understanding of language and its meanings.
• 1950s, beginnings of AI: Initiating the technological development of natural language processing, Alan Turing theorized the future ability of machines and computers to “think” as humans do. When combined with linguistic theory, this theory formed the basis for NLP AI.
• 1957, syntactic structures: Noam Chomsky’s theories of grammar and language development were used to train computers to understand human language.
• 1960s, early computer languages: Programming languages like LISP and ELIZA initiated textual output from computers, with more research taking place on natural language processing.
• 1980s, shift to machine learning: With a major growth in machine learning algorithms that could be applied to computational linguistics, researchers shifted toward statistical models rather than former NLP rules.
• 2000s, the rise of NLPs: With the first neural language model using feedforward neural networks , greater and more rapid development in NLP took place. The rapid introduction of several AI assistants, chatbots and language processors saw natural language processing becoming an everyday technology for people around the world.

What are key types of natural language processing?

There are several key types of natural language processing:

• Whereas supervised NLP methods pertain to known inputs and outputs, unsupervised NLP methods deal with unlabeled inputs. This statement means that supervised NLP categorizes data based on known attributes, using labeled data to train models to predict outcomes accurately. In contrast, unsupervised NLP makes predictions based on dynamic, unknown inputs, identifying patterns and structures within unlabeled data.
• Generative pretrained transformers (GPTs) are a type of artificial intelligence that relies on natural language processing. They can generate textual content based on textual prompts, meaning both input and output require strong contextual processing capabilities of human language. GPTs are designed to understand and produce coherent and contextually relevant text by using vast amounts of pretrained data.
• Entirely rooted in understanding the meaning of verbal content, sentiment analysis is a type of natural language processing wherein computers are trained to extract meaning, tone and feeling from text or speech.
• Machine translation is where computer software is trained in multiple languages to enable systems to translate one set of text (the input) into another language (the output). This ability can expedite translation processes for users.

How is natural language processing used?

A wide range of businesses are using chatbots and AI assistants to alleviate the difficulties of customer service and reduce the need for human intervention. Many websites now use chatbots as the first point of contact, having trained them using natural language processing with a list of solutions to draw from as outputs.

Machine translation is another widely applicable use case. Language translation has become a process that we take for granted. Speedy language translators are at our fingertips, making interlinguistic communication increasingly easy. Natural language processing trains computer translators on all languages and ways in which users might input vocabulary, enabling machine translation.

Natural language processing can also be used to expedite processes and ensure efficiency with administrative tasks across a variety of business types and industries. An example of this is spam detection, meaning that individual people have less obligation to categorize and process the spam that arrives in their inbox.

Another example is event recaps where NLP technology records and transcribes text or speech and processes the meaning to summarize it. This function can be extremely helpful in meetings and learning environments, and it can keep geographically distributed teams in touch without face-to-face contact.