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Universal and language-specific POS tags

NLP pipelines that we practice in class provide two levels of POS information.

Universal POS tags

Universal POS (UPOS) tags provide a relatively small set of categories that can be applied across languages.

Examples include:

UPOS tag General category Example
NOUN common noun student
PROPN proper noun Hawaiʻi
VERB lexical verb analyze
AUX auxiliary may
ADJ adjective complex
ADV adverb carefully
PRON pronoun they
DET determiner the
ADP adposition in
CCONJ coordinating conjunction and
SCONJ subordinating conjunction because
PART particle language-dependent
PUNCT punctuation .

Language-specific part-of-speech tags

A language-specific tag (XPOS) provides a more detailed analysis based on a particular annotation scheme or treebank.

For example, English treebanks often use the Penn Treebank tag set, in which verbs may receive tags such as:

VB   base-form verb
VBD  past-tense verb
VBG  gerund or present participle
VBN  past participle

For Korean, a detailed analysis may distinguish categories such as common nouns, particles, connective endings, and sentence-final endings. Depending on the model and tokenization scheme, a single word may be assigned a combination of multiple Korean-specific tags.

In this tutorial:

  • spaCy’s token.pos_ provides a universal part-of-speech category;
  • spaCy’s token.tag_ provides a more detailed English tag;
  • Stanza’s word.upos provides a universal part-of-speech category;
  • Stanza’s word.xpos provides a treebank-specific Korean tag based on the Sejong tag set.

Further reading

Next: English POS tagging