Trains an unlabeled parser that finds a grammatical relations between two words in a sentence.

For instantiated/pretrained models, see DependencyParserModel.

Dependency parser provides information about word relationship. For example, dependency parsing can tell you what the subjects and objects of a verb are, as well as which words are modifying (describing) the subject. This can help you find precise answers to specific questions.

The required training data can be set in two different ways (only one can be chosen for a particular model):

• Dependency treebank in the Penn Treebank format set with setDependencyTreeBank
• Dataset in the CoNLL-U format set with setConllU

Apart from that, no additional training data is needed.

See DependencyParserApproachTestSpec for further reference on how to use this API.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.sbd.pragmatic.SentenceDetector
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.annotators.pos.perceptron.PerceptronModel
import com.johnsnowlabs.nlp.annotators.parser.dep.DependencyParserApproach
import org.apache.spark.ml.Pipeline

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

val sentence = new SentenceDetector()
  .setInputCols("document")
  .setOutputCol("sentence")

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

val posTagger = PerceptronModel.pretrained()
  .setInputCols("sentence", "token")
  .setOutputCol("pos")

val dependencyParserApproach = new DependencyParserApproach()
  .setInputCols("sentence", "pos", "token")
  .setOutputCol("dependency")
  .setDependencyTreeBank("src/test/resources/parser/unlabeled/dependency_treebank")

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  sentence,
  tokenizer,
  posTagger,
  dependencyParserApproach
))

// Additional training data is not needed, the dependency parser relies on the dependency tree bank / CoNLL-U only.
val emptyDataSet = Seq.empty[String].toDF("text")
val pipelineModel = pipeline.fit(emptyDataSet)