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Let's say there is a sentence:

On March 1, he was born.

Changing it to

He was born on March 1.

doesn't break the sense of the sentence and it is still valid. Shuffling words in any other way would produce weird to invalid sentences. So basically, I'm talking about parts of the sentence, which make the information more specific, but removing them doesn't break the whole sentence. Is there any NLP library in which identifying such parts are available?

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First-order logic is a way of knowledge representation in artificial intelligence. It is an extension of propositional logic.

FOL is sufficiently expressive to represent the natural language statements in a concise way.

First-order logic (like natural language) does not only assume that the world contains facts like propositional logic but also assumes the following things in the world:

  • Objects: A, B, people, numbers, colors, wars, theories, squares, pits, etc

  • Relations: It can be unary relation such as: red, round, is adjacent, or n-any relation such as the sister of, brother of, has color, comes between

  • Function: Father of, best friend, third inning of, end of, etc.

As a natural language, first-order logic has two main parts:

  • Syntax

  • Semantics

For more information regarding the FOL, refer to the following link: https://www.javatpoint.com/first-order-logic-in-artificial-intelligence

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