CRF Project

Project Website: http://crf.sourceforge.net/

Github Link: None

**Description**

The CRF package is a java implementation of Conditional Random Fields for sequential labeling developed by Sunita Sarawagi of IIT Bombay. The package is distributed with the hope that it will be useful for researchers working in information extraction or related areas.

We have attempted to keep the core CRF package compact and barebones for ease of deployment. However, we have packaged additional supporting classes for generating features, managing model structure and dictionary of words in the training data. The best way to learn how to use this code is to follow the examples in the package for Sequence annotations and for Maximum entropy classification Another example of deploying the package can be seen in William Cohen’s Minorthird information extraction toolkit.

We believe this is an efficient implementation of CRFs since it extensively relies on sparse matrix operations and Quasi-Newton optimization during training. Care is taken to avoid memory allocations within core training loops. The support for sparse matrix operations is taken from the COLT distribution and the Quasi-Newton optimization algorithm (LBFGS) is taken from riso.numerical.

Useful Links

The original CRF paper: John Lafferty and Andrew McCallum and Fernando Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, “Proceedings of the International Conference on Machine Learning (ICML-2001)”,2001

The code follows notations and algorithm as described in this paper: F. Sha and F. Pereira, Shallow parsing with conditional random fields, “In Proceedings of HLT-NAACL”, “2003”

The code also includes support for the semi-CRF learner as described in this NIPS paper. Sunita Sarawagi and William W. Cohen. Semi-markov conditional random fields for information extraction. In NIPs, 2004. and, Sunita Sarawagi. Efficient inference on sequence segmentation models. In Proceedings of the 23rd International Conference on Machine Learning (ICML), Pittsburgh, PA, USA, 2006