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Traduction et analyse de mots par intelligence artificielle ChatGPT
Sur cette page, vous pouvez obtenir une analyse détaillée d'un mot ou d'une phrase, réalisée à l'aide de la meilleure technologie d'intelligence artificielle à ce jour:
- comment le mot est utilisé
- fréquence d'utilisation
- il est utilisé plus souvent dans le discours oral ou écrit
- options de traduction de mots
- exemples d'utilisation (plusieurs phrases avec traduction)
- étymologie
Qu'est-ce (qui) est N-entity - définition
N-entity
ACTIVE N-TH LAYER ELEMENT
In telecommunication, a n-entity is an active element in the n-th layer of the Open Systems Interconnection--Reference Model (OSI-RM) that (a) interacts directly with elements, i.e.
Entity linking
THE TASK OF ASSIGNING A UNIQUE IDENTITY TO ENTITIES MENTIONED IN TEXT
Named entity disambiguation; Named entity normalization; Entity Linking
In natural language processing, entity linking, also referred to as named-entity linking (NEL), named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris".
Named-entity recognition
EXTRACTION OF NAMED ENTITY MENTIONS IN UNSTRUCTURED TEXT INTO PRE-DEFINED CATEGORIES
Named Entity Recognition; Entity extraction; Named-entity detection; ENAMEX; Named Entities recognition; Named entity recognition; Named entities; Entity detection; Named entities recognition; Named-entity extraction; Entity chunking
Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
