The following vocabulary correspond to lexic from this article, in the AI portfolio section. 


Keyword extraction is a text analysis technique that automatically extracts the most used and most important words and expressions from a text. It helps summarize the content of texts and recognize the main topics discussed.

Keyword extraction uses machine learning artificial intelligence  with natural language processing to break down human language so that it can be understood and analyzed by machines.

It's used to find keywords from all manner of text: regular documents and business reports, social media comments, online forums and reviews, news reports, and more.


 The keyword match types dictate how closely the keyword needs to match with the user's search query in order for the ad to be considered for the auction. 


Named entity recognition - sometimes referred to as entity chunking, extraction, or identification - is the task of identifying and categorizing key information (entities) in text. An entity can be any word or series of words that consistently refers to the same thing. 

Every detected entity is classified into a predetermined category. For example, an NER machine learning model might detect the word "super.AI" in a text and classify it as a "Company".

NER is a form of natural language processing (NLP), a subfield of artificial intelligence. NLP is concerned with computers processing and analyzing natural language, i.e., any language that has developed naturally, rather than artificially, such as with computer coding languages.

This post explores the basics of how NER works, along with some high-level use cases and how you can apply it in your business or project.