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Type or paste a Lithuanian text into the input box above.
Select a POS analyzer from the left column, then click the "Go" button.
| Example Lithuanian Text for POS Analysis |
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Jaunikis pirmąją vestuvinę naktį iškeitė į areštinės gultą.
Bepiločiai automobiliai išnaikins vairavimo mokyklas, autoservisus ir eismo nelaimes.
Vilniuje galvojama uždrausti naudoti skėčius.
Londonas yra didelis miestas Jungtinėje Karalystėje.
Kur tu?
Kas yra Prancūzijos prezidentas?
Kokia yra Jungtinių Amerikos Valstijų sostinė?
Kada gimė Dalia Grybauskaitė?
A part of speech is a category that describes the role a word plays in a sentence.
Improving Lithuanian 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.
- Lithuanian Part-of-Speech
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UPOS of Lithuanian
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 Lithuanian
XPOS (Detailed POS) is a Fine-Grained tag specific to the Lithuanian language and the Lithuanian 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 Lithuanian 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 Lithuanian 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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