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

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

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 LBFGS, a quasi-newton algorithm for large scale numerical optimization problem
• Linear-chain (first-order Markov) CRF
• Test can run both in parallel and in serial

  val template = Array("U00:%x[-1,0]", "U01:%x[0,0]", "U02:%x[1,0]", "B")
  val train = Array("B-NP|--|Friday|-|NNP\tB-NP|--|'s|-|POS", "I-NP|--|Market|-|NNP\tI-NP|--|Activity|-|NN")
  val test = Array("null|--|Market|-|NNP\tnull|--|Activity|-|NN")

  val trainRdd = sc.parallelize(train).map(Sequence.deSerializer)

  val model = CRF.train(template, trainRdd)
  val result = model.predict(test.map(Sequence.deSerializer))

sbt package

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

• [email protected]
• [email protected]
• [email protected]
• [email protected]