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  • John Snow Labs
  • Jun 19, 2024

Email Regex Matcher

  • en
  • clinical
  • licensed
  • email
  • regexmatcher

Description

This model extracts emails in clinical notes using rule-based RegexMatcherInternal annotator.

Predicted Entities

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How to use

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

sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

regex_matcher = RegexMatcherInternalModel.pretrained("email_matcher","en","clinical/models") \
    .setInputCols(["sentence"])\
    .setOutputCol("email_entity")\

regex_pipeline = Pipeline().setStages([
    documentAssembler,
    sentenceDetector,
    tokenizer,
    regex_matcher])

data = spark.createDataFrame([["""ID: 1231511863, The driver's license no:A334455B, the SSN:324598674 and jadjada_adald19@msku.edu.tr, mail: afakfl_lakf19@yahoo.com, e-mail: hale@gmail.com .
EMAIL: afakfl_lakf19@yahoo.com, E-mail: hale@gmail.com ."""]]).toDF("text")

result = regex_pipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler()
	.setInputCol("text")
	.setOutputCol("document")

val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
	.setInputCols(Array("document"))
	.setOutputCol("sentence")

val tokenizer = new Tokenizer()
	.setInputCols(Array("sentence"))
	.setOutputCol("token")

val regex_matcher = RegexMatcherInternalModel.pretrained("email_matcher","en","clinical/models")
	.setInputCols(Array("sentence"))
	.setOutputCol("email_entity")
	.setMergeOverlapping(true)

val regex_pipeline = new Pipeline().setStages(Array(
		documentAssembler,
		sentenceDetector,
		tokenizer,
		regex_matcher))

val data = Seq(""""ID: 1231511863, The driver's license no:A334455B, the SSN:324598674 and jadjada_adald19@msku.edu.tr, mail: afakfl_lakf19@yahoo.com, e-mail: hale@gmail.com .
EMAIL: afakfl_lakf19@yahoo.com, E-mail: hale@gmail.com .""").toDF("text")

val result = regex_pipeline.fit(data).transform(data)

Results

+---------------------------+-----+---+-----+
|                      chunk|begin|end|label|
+---------------------------+-----+---+-----+
|jadjada_adald19@msku.edu.tr|   72| 98|EMAIL|
|    afakfl_lakf19@yahoo.com|  107|129|EMAIL|
|             hale@gmail.com|  140|153|EMAIL|
|    afakfl_lakf19@yahoo.com|  164|186|EMAIL|
|             hale@gmail.com|  197|210|EMAIL|
+---------------------------+-----+---+-----+

Model Information

Model Name: email_matcher
Compatibility: Healthcare NLP 5.3.3+
License: Licensed
Edition: Official
Input Labels: [sentence]
Output Labels: [email]
Language: en
Size: 6.6 KB