Fits an Annotator to match exact strings or regex patterns provided in a file against a Document and assigns them an named entity. The definitions can contain any number of named entities.

There are multiple ways and formats to set the extraction resource. It is possible to set it either as a "JSON", "JSONL" or "CSV" file. A path to the file needs to be provided to setPatternsResource. The file format needs to be set as the "format" field in the option parameter map and depending on the file type, additional parameters might need to be set.

If the file is in a JSON format, then the rule definitions need to be given in a list with the fields "id", "label" and "patterns":

 [
  {
    "id": "person-regex",
    "label": "PERSON",
    "patterns": ["\\w+\\s\\w+", "\\w+-\\w+"]
  },
  {
    "id": "locations-words",
    "label": "LOCATION",
    "patterns": ["Winterfell"]
  }
]

The same fields also apply to a file in the JSONL format:

{"id": "names-with-j", "label": "PERSON", "patterns": ["Jon", "John", "John Snow"]}
{"id": "names-with-s", "label": "PERSON", "patterns": ["Stark", "Snow"]}
{"id": "names-with-e", "label": "PERSON", "patterns": ["Eddard", "Eddard Stark"]}

In order to use a CSV file, an additional parameter "delimiter" needs to be set. In this case, the delimiter might be set by using .setPatternsResource("patterns.csv", ReadAs.TEXT, Map("format"->"csv", "delimiter" -> "\\|"))

PERSON|Jon
PERSON|John
PERSON|John Snow
LOCATION|Winterfell

Example

In this example, the entities file as the form of

PERSON|Jon
PERSON|John
PERSON|John Snow
LOCATION|Winterfell

where each line represents an entity and the associated string delimited by "|".

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.annotators.er.EntityRulerApproach
import com.johnsnowlabs.nlp.util.io.ReadAs

import org.apache.spark.ml.Pipeline

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

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

val entityRuler = new EntityRulerApproach()
  .setInputCols("document", "token")
  .setOutputCol("entities")
  .setPatternsResource(
    path = "src/test/resources/entity-ruler/patterns.csv",
    readAs = ReadAs.TEXT,
    options = Map("format" -> "csv", "delimiter" -> "\\|")
  )

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  entityRuler
))

val data = Seq("Jon Snow wants to be lord of Winterfell.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(entities)").show(false)
+--------------------------------------------------------------------+
|col                                                                 |
+--------------------------------------------------------------------+
|[chunk, 0, 2, Jon, [entity -> PERSON, sentence -> 0], []]           |
|[chunk, 29, 38, Winterfell, [entity -> LOCATION, sentence -> 0], []]|
+--------------------------------------------------------------------+