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Type or paste a Catalan text into the input box above.
Select a POS analyzer from the left column, then click the "Go" button.
| Example Catalan Text for POS Analysis |
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Apple està buscant comprar una startup del Regne Unit per mil milions de dòlars.
Els cotxes autònoms deleguen la responsabilitat de l'assegurança als seus fabricants.
San Francisco analitza prohibir els robots de repartiment.
Londres és una gran ciutat del Regne Unit.
El gat menja peix
Veig a l'home amb el telescopi
L'aranya menja mosques
El pingüí incuba en el seu niu
A part of speech is a category that describes the role a word plays in a sentence.
Improving Catalan 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.
- Catalan Part-of-Speech
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UPOS of Catalan
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 Catalan
XPOS (Detailed POS) is a Fine-Grained tag specific to the Catalan language and the Catalan 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 Catalan 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 Catalan 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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