|
Type or paste a Slovenian text into the input box above.
Select a POS analyzer from the left column, then click the "Go" button.
| Example Slovenian Text for POS Analysis |
⬆️ |
Apple načrtuje nakup britanskega startupa za 1 bilijon dolarjev.
France Prešeren je umrl 8. februarja 1849 v Kranju.
Staro ljubljansko letališče Moste bo obnovila družba BTC.
London je največje mesto v Združenem kraljestvu.
Kje se skrivaš?
Kdo je predsednik Francije?
Katero je glavno mesto Združenih držav Amerike?
Kdaj je bil rojen Milan Kučan?
A part of speech is a category that describes the role a word plays in a sentence.
Improving Slovenian 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.
- Slovenian Part-of-Speech
-
UPOS of Slovenian
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
-
XPOS of Slovenian
XPOS (Detailed POS) is a Fine-Grained tag specific to the Slovenian language and the Slovenian 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 Slovenian 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 Slovenian 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
|