Voer een woord of zin in in een taal naar keuze 👆
Taal:
Vertaling en analyse van woorden door kunstmatige intelligentie ChatGPT
Op deze pagina kunt u een gedetailleerde analyse krijgen van een woord of zin, geproduceerd met behulp van de beste kunstmatige intelligentietechnologie tot nu toe:
- hoe het woord wordt gebruikt
- gebruiksfrequentie
- het wordt vaker gebruikt in mondelinge of schriftelijke toespraken
- opties voor woordvertaling
- Gebruiksvoorbeelden (meerdere zinnen met vertaling)
- etymologie
Wat (wie) is N-entity - definitie
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.
