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Type or paste a Filipino text into the input box above.
Select a POS analyzer from the left column, then click the "Go" button.
| Example Filipino Text for POS Analysis |
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Teka muna! May mga taga-Maynila na nagbayad ng ₱5,000 para sa nasabing 'status quo'. Ang pinakabatang anak na lalaki, na ang katalinuhan ay kahanga-hanga, ay nakitang nag-aaral nang mas maigi kaysa sa kanyang mga kapatid.
1) Iniisip niyang pinakamabuting angkinin na lang ang anumang aklat na ipinababasa sa kanya;
2) gayunpaman, pilit siyang nakakapagtrabaho nang mabisa kapag tulog ang iba (bihira) — sa totoo lang.
Bakit ba biglang nagsialisan sila? Kung ang X ay katumbas ng Y + 1, nakarating na tayo.
A part of speech is a category that describes the role a word plays in a sentence.
Improving Filipino 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.
- Filipino Part-of-Speech
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UPOS of Filipino
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 Filipino
XPOS (Detailed POS) is a Fine-Grained tag specific to the Filipino language and the Filipino 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 Filipino 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 Filipino 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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