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Type or paste a Spanish text into the input box above.
Select a POS analyzer from the left column, then click the "Go" button.
| Example Spanish Text for POS Analysis |
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Apple está buscando comprar una startup del Reino Unido por mil millones de dólares.
Los coches autónomos delegan la responsabilidad del seguro en sus fabricantes.
San Francisco analiza prohibir los robots de reparto.
Londres es una gran ciudad del Reino Unido.
El gato come pescado.
Veo al hombre con el telescopio.
La araña come moscas.
El pingüino incuba en su nido sobre el hielo.
¿Dónde estáis?
¿Quién es el presidente francés?
¿Dónde se encuentra la capital de Argentina?
¿Cuándo nació José de San Martín?
A part of speech is a category that describes the role a word plays in a sentence.
Improving Spanish 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.
- Spanish Part-of-Speech
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UPOS of Spanish
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 Spanish
XPOS (Detailed POS) is a Fine-Grained tag specific to the Spanish language and the Spanish 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 Spanish 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 Spanish 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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