Type Members

1.  class ContextSpellCheckerApproach extends AnnotatorApproach[ContextSpellCheckerModel] with HasFeatures with WeightedLevenshtein

   Trains a deep-learning based Noisy Channel Model Spell Algorithm.

   Trains a deep-learning based Noisy Channel Model Spell Algorithm. Correction candidates are extracted combining context information and word information.

   For instantiated/pretrained models, see ContextSpellCheckerModel.

   Spell Checking is a sequence to sequence mapping problem. Given an input sequence, potentially containing a certain number of errors, ContextSpellChecker will rank correction sequences according to three things:

   1. Different correction candidates for each word — word level.
   2. The surrounding text of each word, i.e. it’s context — sentence level.
   3. The relative cost of different correction candidates according to the edit operations at the character level it requires — subword level.

   For an in-depth explanation of the module see the article Applying Context Aware Spell Checking in Spark NLP.

   For extended examples of usage, see the article Training a Contextual Spell Checker for Italian Language, the Examples and the ContextSpellCheckerTestSpec.

   Example

   For this example, we use the first Sherlock Holmes book as the training dataset.

   import spark.implicits._
   import com.johnsnowlabs.nlp.base.DocumentAssembler
   import com.johnsnowlabs.nlp.annotators.Tokenizer
   import com.johnsnowlabs.nlp.annotators.spell.context.ContextSpellCheckerApproach
   
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   
   val tokenizer = new Tokenizer()
     .setInputCols("document")
     .setOutputCol("token")
   
   val spellChecker = new ContextSpellCheckerApproach()
     .setInputCols("token")
     .setOutputCol("corrected")
     .setWordMaxDistance(3)
     .setBatchSize(24)
     .setEpochs(8)
     .setLanguageModelClasses(1650)  // dependant on vocabulary size
     // .addVocabClass("_NAME_", names) // Extra classes for correction could be added like this
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     spellChecker
   ))
   
   val path = "src/test/resources/spell/sherlockholmes.txt"
   val dataset = spark.sparkContext.textFile(path)
     .toDF("text")
   val pipelineModel = pipeline.fit(dataset)

   See also

   NorvigSweetingApproach and SymmetricDeleteApproach for alternative approaches to spell checking

2.  class ContextSpellCheckerModel extends AnnotatorModel[ContextSpellCheckerModel] with HasSimpleAnnotate[ContextSpellCheckerModel] with WeightedLevenshtein with WriteTensorflowModel with ParamsAndFeaturesWritable with HasTransducerFeatures with HasEngine

   Implements a deep-learning based Noisy Channel Model Spell Algorithm.

   Implements a deep-learning based Noisy Channel Model Spell Algorithm. Correction candidates are extracted combining context information and word information.

   Spell Checking is a sequence to sequence mapping problem. Given an input sequence, potentially containing a certain number of errors, ContextSpellChecker will rank correction sequences according to three things:

   1. Different correction candidates for each word — word level.
   2. The surrounding text of each word, i.e. it’s context — sentence level.
   3. The relative cost of different correction candidates according to the edit operations at the character level it requires — subword level.

   For an in-depth explanation of the module see the article Applying Context Aware Spell Checking in Spark NLP.

   This is the instantiated model of the ContextSpellCheckerApproach. For training your own model, please see the documentation of that class.

   Pretrained models can be loaded with pretrained of the companion object:

   val spellChecker = ContextSpellCheckerModel.pretrained()
     .setInputCols("token")
     .setOutputCol("checked")

   The default model is "spellcheck_dl", if no name is provided. For available pretrained models please see the Models Hub.

   For extended examples of usage, see the Examples and the ContextSpellCheckerTestSpec.

   Example

   import spark.implicits._
   import com.johnsnowlabs.nlp.DocumentAssembler
   import com.johnsnowlabs.nlp.annotators.Tokenizer
   import com.johnsnowlabs.nlp.annotators.spell.context.ContextSpellCheckerModel
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("doc")
   
   val tokenizer = new Tokenizer()
     .setInputCols(Array("doc"))
     .setOutputCol("token")
   
   val spellChecker = ContextSpellCheckerModel
     .pretrained()
     .setTradeOff(12.0f)
     .setInputCols("token")
     .setOutputCol("checked")
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     spellChecker
   ))
   
   val data = Seq("It was a cold , dreary day and the country was white with smow .").toDF("text")
   val result = pipeline.fit(data).transform(data)
   
   result.select("checked.result").show(false)
   +--------------------------------------------------------------------------------+
   |result                                                                          |
   +--------------------------------------------------------------------------------+
   |[It, was, a, cold, ,, dreary, day, and, the, country, was, white, with, snow, .]|
   +--------------------------------------------------------------------------------+

   See also

   NorvigSweetingModel and SymmetricDeleteModel for alternative approaches to spell checking

3.  trait HasTransducerFeatures extends HasFeatures
4.  case class LangModelSentence(ids: Array[Int], cids: Array[Int], cwids: Array[Int], len: Int) extends Product with Serializable
5.  trait ReadablePretrainedContextSpell extends ReadsLanguageModelGraph with HasPretrained[ContextSpellCheckerModel]
6.  trait ReadsLanguageModelGraph extends ParamsAndFeaturesReadable[ContextSpellCheckerModel] with ReadTensorflowModel
7.  trait WeightedLevenshtein extends AnyRef