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Type or paste a Portuguese text into the input box above.
Select a POS analyzer from the left column, then click the "Go" button.
| Example Portuguese Text for POS Analysis |
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Apple está querendo comprar uma startup do Reino Unido por 100 milhões de dólares.
Carros autônomos empurram a responsabilidade do seguro para os fabricantes.São Francisco considera banir os robôs de entrega que andam pelas calçadas.
Londres é a maior cidade do Reino Unido.
A part of speech is a category that describes the role a word plays in a sentence.
Improving Portuguese 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.
- Portuguese Part-of-Speech
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UPOS of Portuguese
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 Portuguese
XPOS (Detailed POS) is a Fine-Grained tag specific to the Portuguese language and the Portuguese 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 Portuguese 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 Portuguese 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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