Description
Labels tokens with their universal POS (UPOS) tags, treebank-specific POS (XPOS) tags, and universal morphological features (UFeats).
| Property name |
Annotator class name |
Generated Annotation |
| pos |
POSProcessor |
UPOS, XPOS, and UFeats annotations accessible through Word’s properties pos, xpos, and ufeats. |
Options
| Option name |
Type |
Default |
Description |
| pos_batch_size |
int |
5000 |
When annotating, this argument specifies the maximum number of words to process as a minibatch for efficient processing.
Caveat: the larger this number is, the more working memory is required (main RAM or GPU RAM, depending on the computating device). This parameter should be set larger than the number of words in the longest sentence in your input document, or you might run into unexpected behaviors. |
Example Usage
Running the part of speech tagger simply requires tokenization and multi-word expansion. So the pipeline
can be run with tokenize,mwt,pos as the list of processors. After the pipeline is run, the document will
contain a list of sentences, and the sentences will contain lists of words. The part-of-speech tags can
be accessed via the upos and xpos fields.
import stanfordnlp
nlp = stanfordnlp.Pipeline(processors='tokenize,mwt,pos')
doc = nlp("Barack Obama was born in Hawaii.")
print(*[f'word: {word.text+" "}\tupos: {word.upos}\txpos: {word.xpos}' for sent in doc.sentences for word in sent.words], sep='\n')
This code will generate the following output:
word: Barack upos: PROPN xpos: NNP
word: Obama upos: PROPN xpos: NNP
word: was upos: AUX xpos: VBD
word: born upos: VERB xpos: VBN
word: in upos: ADP xpos: IN
word: Hawaii upos: PROPN xpos: NNP
word: . upos: PUNCT xpos: .
Training-Only Options
Most training-only options are documented in the argument parser of the POS/UFeats tagger.