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Type or paste a French text into the input box above.
Select a POS analyzer from the left column, then click the "Go" button.
| Example French Text for POS Analysis |
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Apple cherche à acheter une start-up anglaise pour 1 milliard de dollars.
Les voitures autonomes déplacent la responsabilité de l'assurance vers les constructeurs.
San Francisco envisage d'interdire les robots coursiers sur les trottoirs.
Londres est une grande ville du Royaume-Uni.
L’Italie choisit ArcelorMittal pour reprendre la plus grande aciérie d’Europe.
Apple lance HomePod parce qu'il se sent menacé par l'Echo d'Amazon.
La France ne devrait pas manquer d'électricité cet été, même en cas de canicule.
Nouvelles attaques de Trump contre le maire de Londres.
Où es-tu?
Qui est le président de la France?
Où est la capitale des États-Unis?
Quand est né Barack Obama?
A part of speech is a category that describes the role a word plays in a sentence.
Improving French language learning using Part-of-Speech (POS) tagging involves leveraging syntactic and morphological information to understand sentence structure, disambiguate word meanings, and master inflectional rules.
- French Part-of-Speech
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UPOS of French
UPOS (Universal POS) is a Coarse-grained and simplified tag that work consistently across all languages. They are shown in the following format.
Headword lemma UPOS DEP 👤NER
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XPOS of French
XPOS (Detailed POS) is a Fine-Grained tag specific to the French language and the French training data. They are shown in the following format.
Headword lemma XPOS DEP 👤NER
Headword : Headwords are displayed in bold.
lemma : The dictionary form or "root" of a French word. It removes grammatical variations. The lemma is only displayed if the headword is not equal to the lemma.
UPOS : Universal Part-of-Speech. A coarse-grained, standardized tag (like NOUN, VERB, or ADJ) designed to work across all human languages. See examples
XPOS : Language-Specific Part-of-Speech. A fine-grained tag specific to a particular French language’s grammar (e.g., distinguishing a plural noun from a singular noun, etc). See examples
DEP : Dependency. The grammatical relationship between words. It shows how words depend on one another, such as identifying which word is the subject (nsubj) or the direct object (obj). See examples
👤NER : Named Entity Recognition. The identification of ""real-world"" entities within the text, such as People (PER), Locations (GPE), Organizations (ORG), or Dates. See examples
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