Probabilistic latent semantic indexing

Probabilistic latent semantic indexing is a way of organizing words and ideas in a way that helps computers (and people!) understand what text is about. It's like when you play with blocks and put all the blocks that look similar together.

When you read a book or a webpage, there are lots of words, and each word can mean many different things. For example, the word "bat" could be talking about the flying mammal, or a baseball bat used in a game. Probabilistic latent semantic indexing helps to figure out which meaning of a word is being used in a particular sentence.

It works by looking at all the words in a large collection of text and trying to figure out which words are related to each other. Then, it groups these related words together into what are called "topics." For example, one topic might be about animals, and another topic might be about sports.

Once the computer has figured out which words are in which topics, it can analyze a new piece of text and figure out which topics are being discussed. This can be really helpful when you're searching for information or trying to understand what a piece of text is about.

In summary, probabilistic latent semantic indexing helps computers understand what a piece of writing is talking about by organizing words into groups based on similar meanings.