POS Tagger Clinical

Description

Sets a Part-Of-Speech tag to each word within a sentence.

Download

How to use

...
pos = PerceptronModel.pretrained("pos_clinical","en","clinical/models")\
	.setInputCols(["token","sentence"])\
	.setOutputCol("pos")
    
pos_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos])
light_pipeline = LightPipeline(pos_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")))
result = light_pipeline.fullAnnotate("""He was given boluses of MS04 with some effect, he has since been placed on a PCA - he take 80mg of oxycontin at home, his PCA dose is ~ 2 the morphine dose of the oxycontin, he has also received ativan for anxiety.""")
val pos = PerceptronModel.pretrained("pos_clinical","en","clinical/models")
	.setInputCols("token","sentence")
	.setOutputCol("pos")
    
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos))
val result = pipeline.fit(Seq.empty["He was given boluses of MS04 with some effect, he has since been placed on a PCA - he take 80mg of oxycontin at home, his PCA dose is ~ 2 the morphine dose of the oxycontin, he has also received ativan for anxiety."].toDS.toDF("text")).transform(data)

Results

[Annotation(pos, 0, 1, NN, {'word': 'He'}),
 Annotation(pos, 3, 5, VBD, {'word': 'was'}),
 Annotation(pos, 7, 11, VVN, {'word': 'given'}),
 Annotation(pos, 13, 19, NNS, {'word': 'boluses'}),
 Annotation(pos, 21, 22, II, {'word': 'of'}),
...]

Model Information

Name: pos_clinical
Type: PerceptronModel
Compatibility: Spark NLP 2.0.2+
License: Licensed
Edition: Official
Input labels: [token, sentence]
Output labels: [pos]
Language: en
Dependencies: embeddings_clinical

Data Source

Trained with MedPost dataset.