Description
This model annotates the part of speech of tokens in a text. The parts of speech annotated include PRON (pronoun), CCONJ (coordinating conjunction), and 15 others. The part of speech model is useful for extracting the grammatical structure of a piece of text automatically.
Open in Colab Download Copy S3 URI
How to use
...
pos = PerceptronModel.pretrained("pos_ud_bdt", "eu") \
.setInputCols(["document", "token"]) \
.setOutputCol("pos")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
results = light_pipeline.fullAnnotate("Iparraldeko erregea izateaz gain, mediku ingelesa eta anestesia eta higiene medikoa garatzen duen liderra da John Snow.")
...
val pos = PerceptronModel.pretrained("pos_ud_bdt", "eu")
.setInputCols(Array("document", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos))
val data = Seq("Iparraldeko erregea izateaz gain, mediku ingelesa eta anestesia eta higiene medikoa garatzen duen liderra da John Snow.").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["""Iparraldeko erregea izateaz gain, mediku ingelesa eta anestesia eta higiene medikoa garatzen duen liderra da John Snow."""]
pos_df = nlu.load('eu.pos').predict(text, output_level='token')
pos_df
Results
[Row(annotatorType='pos', begin=0, end=10, result='NOUN', metadata={'word': 'Iparraldeko'}),
Row(annotatorType='pos', begin=12, end=18, result='NOUN', metadata={'word': 'erregea'}),
Row(annotatorType='pos', begin=20, end=26, result='VERB', metadata={'word': 'izateaz'}),
Row(annotatorType='pos', begin=28, end=31, result='NOUN', metadata={'word': 'gain'}),
Row(annotatorType='pos', begin=32, end=32, result='PUNCT', metadata={'word': ','}),
...]
Model Information
| Model Name: | pos_ud_bdt |
| Type: | pos |
| Compatibility: | Spark NLP 2.5.5+ |
| Edition: | Official |
| Input labels: | [token] |
| Output labels: | [pos] |
| Language: | eu |
| Case sensitive: | false |
| License: | Open Source |
Data Source
The model is imported from https://universaldependencies.org