Annotator to match exact phrases (by token) provided in a file against a Document.

A text file of predefined phrases must be provided with setStoragePath. The text file can als be set directly as an ExternalResource.

In contrast to the normal TextMatcher, the BigTextMatcher is designed for large corpora.

For extended examples of usage, see the BigTextMatcherTestSpec.

Example

In this example, the entities file is of the form

...
dolore magna aliqua
lorem ipsum dolor. sit
laborum
...

where each line represents an entity phrase to be extracted.

import spark.implicits._
import com.johnsnowlabs.nlp.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.Tokenizer
import com.johnsnowlabs.nlp.annotator.BigTextMatcher
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 data = Seq("Hello dolore magna aliqua. Lorem ipsum dolor. sit in laborum").toDF("text")
val entityExtractor = new BigTextMatcher()
  .setInputCols("document", "token")
  .setStoragePath("src/test/resources/entity-extractor/test-phrases.txt", ReadAs.TEXT)
  .setOutputCol("entity")
  .setCaseSensitive(false)

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, entityExtractor))
val results = pipeline.fit(data).transform(data)
results.selectExpr("explode(entity)").show(false)
+--------------------------------------------------------------------+
|col                                                                 |
+--------------------------------------------------------------------+
|[chunk, 6, 24, dolore magna aliqua, [sentence -> 0, chunk -> 0], []]|
|[chunk, 53, 59, laborum, [sentence -> 0, chunk -> 1], []]           |
+--------------------------------------------------------------------+