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_lvtb", "lv") \
.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("Džons Snovs ir ne tikai ziemeļu karalis, bet arī angļu ārsts un anestēzijas un medicīniskās higiēnas attīstības līderis.")
val pos = PerceptronModel.pretrained("pos_ud_lvtb", "lv")
.setInputCols(Array("document", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos))
val data = Seq("Džons Snovs ir ne tikai ziemeļu karalis, bet arī angļu ārsts un anestēzijas un medicīniskās higiēnas attīstības līderis.").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["""Džons Snovs ir ne tikai ziemeļu karalis, bet arī angļu ārsts un anestēzijas un medicīniskās higiēnas attīstības līderis."""]
pos_df = nlu.load('lv.pos').predict(text, output_level='token')
pos_df
Results
[Row(annotatorType='pos', begin=0, end=4, result='PROPN', metadata={'word': 'Džons'}),
Row(annotatorType='pos', begin=6, end=10, result='PROPN', metadata={'word': 'Snovs'}),
Row(annotatorType='pos', begin=12, end=13, result='AUX', metadata={'word': 'ir'}),
Row(annotatorType='pos', begin=15, end=16, result='CCONJ', metadata={'word': 'ne'}),
Row(annotatorType='pos', begin=18, end=22, result='CCONJ', metadata={'word': 'tikai'}),
...]
Model Information
| Model Name: | pos_ud_lvtb |
| Type: | pos |
| Compatibility: | Spark NLP 2.5.5+ |
| Edition: | Official |
| Input labels: | [token] |
| Output labels: | [pos] |
| Language: | lv |
| Case sensitive: | false |
| License: | Open Source |
Data Source
The model is imported from https://universaldependencies.org