CRF

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.


References in zbMATH (referenced in 1 article )

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  1. Aggarwal, Charu C.: Machine learning for text (2018)