Natural Language Processing (NLP)
Understand how natural language processing (NLP) enables machines to interpret human language. Learn key concepts such as named entity recognition, sentiment analysis, and part-of-speech tagging. Discover real-world uses in search engines, chatbots, translation, and recommendation systems, and explore popular Python NLP libraries for practical applications.
In recent years, various branches of AI, including natural language processing (NLP), have garnered considerable attention. Nowadays, everyone knows about ChatGPT, even if they haven’t heard about any other generative AI tools. There’s a lot of science behind them, including NLP.
But what exactly is NLP and how can machines decode human language?
What is NLP?
NLP encompasses various disciplines, including computer science, linguistics, mathematics, and psychology, leveraging their techniques. Its objective is to create systems capable of comprehending and analyzing human language. This form of communication is intricate, involving multiple layers of meaning and subtlety.
NLP focuses on analyzing text, speech, and other forms of human-generated language data. Human language is intricate, encompassing references, real-world allusions, cultural nuances, and personal experiences. Consider the following sentence as an example: “I started a new job near in Chicago where my sister lives.”
The statement contains a reference to a person, a location, and time. NLP techniques employ different statistical models to effectively process the text and accurately determine which part of the sentence refers to a person, which to a city, and which to time.
Applications of NLP
While NLP has gained recent popularity, it has ...