CRF++ is a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data. CRF++ is designed for generic purpose and will be applied to a variety of NLP tasks, such as Named Entity Recognition, Information Extraction and Text Chunking.
- Can redefine feature sets
- Written in C++ with STL
- Fast training based on LBFGS, a quasi-newton algorithm for large scale numerical optimization problem
- Less memory usage both in training and testing encoding/decoding in practical time
- Can perform n-best outputs
- Can perform single-best MIRA training
- Can output marginal probabilities for all candidates
- Available as an open source software
- Changes to previous version:
Initial Announcement on mloss.org.
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