Part of Speech for a Classical Chinese text

Type or paste a Classical Chinese text into the input box above.
Select a POS analyzer from the left column, then click the "Go" button.

Example Classical Chinese Text for POS Analysis ⬆️
樹影初濃夏日遲 晩風生自拂雲枝
君不見 黃河之水天上來 奔流到海不復回
少小離家老大回 鄕音不改鬂毛衰 兒童相見不相識 笑問客從何處來
少年易老學難成 一寸光陰不可輕
學而時習之 不亦說乎 有朋自遠方來 不亦樂乎
A part of speech is a category that describes the role a word plays in a sentence. Improving Classical Chinese 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.
Classical Chinese Part-of-Speech
UPOS of Classical Chinese
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 Classical Chinese
XPOS (Detailed POS) is a Fine-Grained tag specific to the Classical Chinese language and the Classical Chinese 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 Classical Chinese 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 Classical Chinese 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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