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Entailment in ML - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In machine learning, entailment refers to a relationship between two statements or propositions, where the truth of one statement logically implies the truth of another. This concept is particularly relevant in natural language processing (NLP) and text-based applications. The symbol commonly used to represent entailment is "⇒" (a rightward-pointing arrow). There are three main types of entailment relationships: i) Positive Entailment: If statement A positively entails statement B, it means that whenever statement A is true, statement B must also be true. In other words, the truth of A implies the truth of B. Example: A: "The cat is on the mat." B: "There is a mat." If A is true (the cat is on the mat), then B must also be true (there is a mat). ii) Negative Entailment: If statement A negatively entails statement B, it means that whenever statement A is false, statement B must be true. The falsity of A implies the truth of B. Example: A: "It is not raining outside." B: "The ground is dry." If A is false (it is raining outside), then B must be true (the ground is dry). iii) Mutual Entailment: If statements A and B mutually entail each other, it means that the truth of A implies the truth of B, and vice versa. Example: A: "The door is open." B: "The room is not closed." If A is true (the door is open), then B must be true (the room is not closed), and if B is true, then A must also be true.
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