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  • John Snow Labs
  • Mar 08, 2021

Part of Speech for Italian

  • part_of_speech
  • open_source
  • italian
  • pos_ud_isdt
  • it

Description

A Part of Speech classifier predicts a grammatical label for every token in the input text. Implemented with a averaged perceptron architecture.

Predicted Entities

  • PROPN
  • PUNCT
  • NOUN
  • ADP
  • ADJ
  • DET
  • AUX
  • VERB
  • PRON
  • CCONJ
  • NUM
  • ADV
  • INTJ
  • SCONJ
  • X
  • SYM
  • PART

Live Demo Open in Colab Download Copy S3 URI

How to use


document_assembler = DocumentAssembler() \
  .setInputCol("text") \
  .setOutputCol("document")

sentence_detector = SentenceDetector() \
  .setInputCols(["document"]) \
  .setOutputCol("sentence")

pos = PerceptronModel.pretrained("pos_ud_isdt", "it") \
  .setInputCols(["document", "token"]) \
  .setOutputCol("pos")

pipeline = Pipeline(stages=[
  document_assembler,
  sentence_detector,
  posTagger
])

example = spark.createDataFrame([['Ciao da John Snow Labs! ']], ["text"])

result = pipeline.fit(example).transform(example)




val document_assembler = DocumentAssembler()
        .setInputCol("text")
        .setOutputCol("document")

val sentence_detector = SentenceDetector()
        .setInputCols("document")
.setOutputCol("sentence")

val pos = PerceptronModel.pretrained("pos_ud_isdt", "it")
        .setInputCols(Array("document", "token"))
        .setOutputCol("pos")

val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, pos))

val data = Seq("Ciao da John Snow Labs! ").toDF("text")
val result = pipeline.fit(data).transform(data)



import nlu
text = [""Ciao da John Snow Labs! ""]
token_df = nlu.load('it.pos.ud_isdt').predict(text)
token_df

Results

  token    pos
              
0  Ciao   VERB
1    da    ADP
2  John  PROPN
3  Snow  PROPN
4  Labs  PROPN
5     !  PUNCT

Model Information

Model Name: pos_ud_isdt
Compatibility: Spark NLP 3.0.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [pos]
Language: it