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.
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How to use
...
pos = PerceptronModel.pretrained("pos_ud_ytb", "yo") \
.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("Yato si jijẹ ọba ariwa, John Snow jẹ oṣoogun ara Gẹẹsi kan ati adari ninu idagbasoke anaesthesia ati imototo ilera.")
...
val pos = PerceptronModel.pretrained("pos_ud_ytb", "yo")
.setInputCols(Array("document", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos))
val data = Seq("Yato si jijẹ ọba ariwa, John Snow jẹ oṣoogun ara Gẹẹsi kan ati adari ninu idagbasoke anaesthesia ati imototo ilera.").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["""Yato si jijẹ ọba ariwa, John Snow jẹ oṣoogun ara Gẹẹsi kan ati adari ninu idagbasoke anaesthesia ati imototo ilera."""]
pos_df = nlu.load('yo.pos').predict(text)
pos_df
Results
[Row(annotatorType='pos', begin=0, end=3, result='NOUN', metadata={'word': 'Yato'}),
Row(annotatorType='pos', begin=5, end=6, result='VERB', metadata={'word': 'si'}),
Row(annotatorType='pos', begin=8, end=11, result='VERB', metadata={'word': 'jijẹ'}),
Row(annotatorType='pos', begin=13, end=15, result='NOUN', metadata={'word': 'ọba'}),
Row(annotatorType='pos', begin=17, end=21, result='NOUN', metadata={'word': 'ariwa'}),
...]
Model Information
| Model Name: | pos_ud_ytb |
| Type: | pos |
| Compatibility: | Spark NLP 2.5.5+ |
| Edition: | Official |
| Input labels: | [token] |
| Output labels: | [pos] |
| Language: | yo |
| Case sensitive: | false |
| License: | Open Source |
Data Source
The model is imported from https://universaldependencies.org