It's a statistic for evaluating the effectiveness of language models. It is described MATHEMATICALLY as a function of the likelihood that the language model describes a test sample. The perplexity of a test sample X = x1, x2, x3,....,xn is given by,

PP(X)=P(x1,x2,…,xN)-1N

The total number of word tokens is N.

The more perplexing the situation, the less information the language model conveys.

One of the most IMPORTANT reasons for NLP is that it allows computers to converse with people in natural language. Other language-related activities are also scaled. Computers can now hear, analyse, QUANTIFY, and identify which parts of speech are significant thanks to Natural Language Processing (NLP). NLP has a wide range of applications, including chatbots, sentiment analysis, and market intelligence. Since its introduction, NLP has grown in POPULARITY. Today, devices like Amazon's Alexa are extensively used all over the world. And, for businesses, business intelligence and consumer monitoring are quickly gaining traction and will soon rule the industry.

References and Resources:

• Natural Language Processing with Python – Book by Edward Loper, Ewan Klein, and Steven Bird (Published by: O'Reilly Media, Inc.)
• Practical Natural Language Processing – By Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana (Published by: O'Reilly Media, Inc.)
• Natural Language Processing in ACTION: Understanding, Analyzing, and Generating Text with Python – Book by Cole Howard, Hannes Hapke, and Hobson Lane