##Why Visual Studio The CRF++ project is very famous in the research and industry domains. But it's hard to get the CRF++-0.58(The latest version) source code and build it in Windows. So today I build it with Visual Studio in Windows and hope it can help you.

##Introduction 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.

##Features

• 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

##Train(encoding) Use crf_learn command:

crf_learn template_file train_file model_file

where template_file and train_file are the files you need to prepare in advance. crf_learn generates the trained model file in model_file.

crf_learn outputs the following information.

CRF++: Yet Another CRF Tool Kit
Copyright(C) 2005 Taku Kudo, All rights reserved.

reading training data: 100.. 200.. 300.. 400.. 500.. 600.. 700.. 800.. 
Done! 1.94 s

Number of sentences: 823
Number of features:  1075862
Number of thread(s): 1
Freq:                1
eta:                 0.00010
C:                   1.00000
shrinking size:      20
Algorithm:           CRF

iter=0 terr=0.99103 serr=1.00000 obj=54318.36623 diff=1.00000
iter=1 terr=0.35260 serr=0.98177 obj=44996.53537 diff=0.17161
iter=2 terr=0.35260 serr=0.98177 obj=21032.70195 diff=0.53257
iter=3 terr=0.23879 serr=0.94532 obj=13642.32067 diff=0.35138
iter=4 terr=0.15324 serr=0.88700 obj=8985.70071 diff=0.34134
iter=5 terr=0.11605 serr=0.80680 obj=7118.89846 diff=0.20775
iter=6 terr=0.09305 serr=0.72175 obj=5531.31015 diff=0.22301
iter=7 terr=0.08132 serr=0.68408 obj=4618.24644 diff=0.16507
iter=8 terr=0.06228 serr=0.59174 obj=3742.93171 diff=0.18953

• iter: number of iterations processed
• terr: error rate with respect to tags. (# of error tags/# of all tag)
• serr: error rate with respect to sentences. (# of error sentences/# of all sentences)
• obj: current object value. When this value converges to a fixed point, CRF++ stops the iteration.
• diff: relative difference from the previous object value.

##Testing (decoding) Use crf_test command:

crf_test -m model_file test_files ...

where model_file is the file crf_learncreates. In the testing, you don't need to specify the template file, because the model file has the same information for the template. test_file is the test data you want to assign sequential tags. This file has to be written in the same format as training file.

Here is an output of crf_test:

Rockwell        NNP     B       B
International   NNP     I       I
Corp.   NNP     I       I
's      POS     B       B
Tulsa   NNP     I       I
unit    NN      I       I
..