CRF-Spark

A Spark-based implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data.

Requirements

This documentation is for Spark 1.4+. Other version will probably work yet not tested.

Features

CRF-Spark provides following features:

Training in parallel based on Spark RDD
Support a simple format of training and test file. Any other format also can be read by a simple tokenizer.
A common feature templates design, which is also used in other machine learning tools, such as CRF++ and miralium
Fast training based on Limited-memory BFGS optimizaton algorithm (fitting L2-regularized models) or Orthant-wise Limited Memory Quasi-Newton optimizaton algorithm (fitting L1-regularized models)
Support two verbose levels to provide extra information. VerboseLevel1 shows marginal probabilities for each tag and a conditional probability for the entire output; VerboseLevel2 shows marginal probabilities for all other candidates additionally.
Support n-best outputs
Linear-chain (first-order Markov) CRF
Test can run both in parallel and in serial

Applications

A web-based application of this package to Chinese POS Tagging.

Example

Scala API

  val template = Array("U00:%x[-1,0]", "U01:%x[0,0]", "U02:%x[1,0]", "B")

  val train1 = Array("B-NP|--|Friday|-|NNP\tB-NP|--|'s|-|POS", "I-NP|--|Market|-|NNP\tI-NP|--|Activity|-|NN")
  val test1 = Array("null|--|Market|-|NNP\tnull|--|Activity|-|NN")
  val trainRdd1 = sc.parallelize(train).map(Sequence.deSerializer)
  val model1 = CRF.train(template, trainRdd)
  val result1 = model.predict(test.map(Sequence.deSerializer))
  
  val train2 = Array("Friday NNP B-NP\nB-NP 's POS", "Market NNP I-NP\nActivity NN I-NP")
  val test2 = Array("Market NNP\nActivity NN")
  val trainRdd2 = sc.parallelize(train2).map(sentence => {
    val tokens = sentence.split("\n")
    Sequence(tokens.map(token => {
      val tags: Array[String] = token.split(' ')
      Token.put(tags.last, tags.dropRight(1))
    }))
  })
  val model2 = CRF.train(template, trainRdd2)
  val result2 = model2.predict(test2.map(sentence => {
    val tokens = sentence.split("\n")
    Sequence(tokens.map(token => {
      val tags: Array[String] = token.split(' ')
      Token.put(tags)
    }))
  }))

Building From Source

sbt package

Contact & Feedback

If you encounter bugs, feel free to submit an issue or pull request. Also you can mail to:

zDanica/CRF-Spark