CRFSuite Changelog

What's new in CRFSuite 0.12

• [CORE] Optimized the implementation for faster training; approximately 1.4-1.5 x speed up.
• [CORE] Faster routine for computing exp(x) using SSE2.
• [CORE] Restructured the source code to separate routines for CRF graphical models and training algorithms; this is an initial attempt for implementing CRFs with different feature types (e.g., 2nd-order CRF, 1st-order transition features conditioned on observations) and different training algorithms.
• [CORE] Implemented new training algorithms: Averaged Perceptron, Passive Aggressive, and Adaptive Regularization of Weights (AROW).
• [CORE] Removed automatic generation of BOS/EOS features; one can use these features by inserting attributes to the first/last items (e.g., "__BOS__" at the first item and "__EOS__" at the last item).
• [CORE] Fixed some memory-leak problems.
• [CORE] Reduced memory usage in training.
• [CORE] Fixed a crash problem when the model file does not exist in tagging.
• [FRONTEND:LEARN] Training and test sets are maintained by group numbers; specify the group number for hold-out evaluation with "-e" option.
• [FRONTEND:LEARN] Training algorithm is now specified by "-a" option instead of "-p algorithm=".
• [FRONTEND:LEARN] Renamed some training parameters; for example, an L2 regularization coefficient is specified by "c2" instead of "regularization.sigma" (c2 = 0.5 / sigma * sigma; c1 = 1.0 / sigma).
• [FRONTEND:LEARN] Show the list of parameters, default values, and descriptions with "-H" option.
• [FRONTEND:LEARN] Removed the support of comment lines for simplicity; one may forget to escape '#' characters in a data set. CRFsuite now does not handle '#' as a special character.
• [FRONTEND:TAGGER] Output probabilities of predicted sequences with "-p" option.
• [FRONTEND:TAGGER] Output marginal probabilities of predicted items with "-i" option.
• [API] Numerous changes in API for the enhancements.
• [API] Renamed the library name "libcrf" to "libcrfsuite".
• [API] Renamed the prefix "crf_" to "crfsuite_" in structure and function names.
• [API] Implemented a high-level and easy-to-use API for C++/SWIG (crfsuite.hpp and crfsuite_api.hpp).
• [API] Implemented the Python SWIG module and sample programs; writing a tagger is very easy with this module.
• [SAMPLE] Rewritten samples.
• [SAMPLE] A sample program (template.py) for using feature templates that are compatible with CRF++.
• [SAMPLE] New samples in example directory: Named Entity Recognition (ner.py) using the CoNLL2003 data set, and part-of-speech tagging (pos.py).
• [OTHER] Updated the MSVC solution file to MSVC 2010.