Type Members

1.  class AhoCorasickAutomaton extends Serializable

   Aho-Corasick Algorithm: https://dl.acm.org/doi/10.1145/360825.360855 A simple, efficient algorithm to locate all occurrences of any of a finite number of keywords in a string of text.

   Aho-Corasick Algorithm: https://dl.acm.org/doi/10.1145/360825.360855 A simple, efficient algorithm to locate all occurrences of any of a finite number of keywords in a string of text. The algorithm consists of constructing a finite state pattern matching machine from the keywords and then using the pattern matching machine to process the text string in a single pass. The complexity of constructing a pattern matching machine and searching the text is linear to the total length of given patterns and the length of a text, respectively.

2.  case class EntityPattern(label: String, patterns: Seq[String], id: Option[String] = None, regex: Option[Boolean] = None) extends Product with Serializable
3.  class EntityRulerApproach extends AnnotatorApproach[EntityRulerModel] with HasStorage

   Fits an Annotator to match exact strings or regex patterns provided in a file against a Document and assigns them an named entity.

   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], []]|
   +--------------------------------------------------------------------+

4.  case class EntityRulerFeatures(patterns: Map[String, String], regexPatterns: Map[String, Seq[String]]) extends Serializable with Product
5.  class EntityRulerModel extends AnnotatorModel[EntityRulerModel] with HasSimpleAnnotate[EntityRulerModel] with HasStorageModel

   Instantiated model of the EntityRulerApproach.

   Instantiated model of the EntityRulerApproach. For usage and examples see the documentation of the main class.

6.  case class FlattenEntityPattern(entity: String, keyword: String, id: Option[String] = None) extends Product with Serializable
7.  class PatternsReadWriter extends PatternsReader with StorageReadWriter[String]
8.  class PatternsReader extends StorageReader[String]
9.  trait ReadablePretrainedEntityRuler extends StorageReadable[EntityRulerModel] with HasPretrained[EntityRulerModel]
10.  class RegexPatternsReadWriter extends RegexPatternsReader with StorageReadWriter[Seq[String]]
11.  class RegexPatternsReader extends StorageReader[Seq[String]]