Natural Language Processing (NLP) is a practice in which computers are taught to process, understand and replicate natural human speech. As a discipline, it combines elements of computer science, computational linguistics, deep learning, artificial intelligence (AI) and machine learning (ML). NLP depends on the ability to ingest, process and analyze massive amounts of human speech — in written and verbal form — to interpret meaning and respond correctly. The ultimate goal of NLP is to allow humans to communicate with computers and devices as closely as possible to the way they interact with other humans.
The concept of NLP has existed since the 1950s, when computing pioneer Alan Turing proposed what he called the “imitation game” (later known as the Turing test), in which a human operator asks a series of questions through a text-only channel to determine if an unseen respondent is a human or computer. If the human can’t tell, the computer has “passed the Turing test,” which is often described as the ultimate goal of AI or NLP.
NLP powers applications from automated telephone response trees to speech-to-text to GPS systems to automated assistants such as Amazon’s Alexa, Apple’s Siri, Microsoft Azure and Google Assistant. It can be used to perform automated translation of text from one language to another, to respond to verbal commands as in the case of virtual assistants, to analyze and summarize large amounts of text and much more.
In this article, we’ll discuss the types of NLP, how they work, some common NLP tasks and applications and talk about how artificial intelligence (AI) and machine learning (ML) contribute to NLP. We’ll also take a look at the challenges and benefits of NLP and how it may evolve in the future.