CRF++: Yet Another CRF toolkit

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.

Table of contents

Features

News

Download

Installation

Usage

Training and Test file formats

Both the training file and the test file need to be in a particular format for CRF++ to work properly. Generally speaking, training and test file must consist of multiple tokens. In addition, a token consists of multiple (but fixed-numbers) columns. The definition of tokens depends on tasks, however, in most of typical cases, they simply correspond to words. Each token must be represented in one line, with the columns separated by white space (spaces or tabular characters). A sequence of token becomes a sentence. To identify the boundary between sentences, an empty line is put.

You can give as many columns as you like, however the number of columns must be fixed through all tokens. Furthermore, there are some kinds of "semantics" among the columns. For example, 1st column is 'word', second column is 'POS tag' third column is 'sub-category of POS' and so on.

The last column represents a true answer tag which is going to be trained by CRF.

Here's an example of such a file: (data for CoNLL shared task)

He        PRP  B-NP
reckons   VBZ  B-VP
the       DT   B-NP
current   JJ   I-NP
account   NN   I-NP
deficit   NN   I-NP
will      MD   B-VP
narrow    VB   I-VP
to        TO   B-PP
only      RB   B-NP
#         #    I-NP
1.8       CD   I-NP
billion   CD   I-NP
in        IN   B-PP
September NNP  B-NP
.         .    O

He        PRP  B-NP
reckons   VBZ  B-VP
..

There are 3 columns for each token.

The following data is invalid, since the number of columns of second and third are 2. (They have no POS column.) The number of columns should be fixed.

He        PRP  B-NP
reckons   B-VP
the       B-NP
current   JJ   I-NP
account   NN   I-NP
..

Preparing feature templates

As CRF++ is designed as a general purpose tool, you have to specify the feature templates in advance. This file describes which features are used in training and testing.

Training (encoding)

Use crf_learn command: 

% crf_learn template_file train_file model_file

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

Typically, crf_learn outputs the following information to STDOUT. Also, crf_learn shows additional information for each iteration of LBFGS routine.

% crf_learn template_file train_file model_file
CRF++: Yet Another CRF Tool Kit
Copyright (C) 2005 Taku Kudo, All rights reserved.

reading training data: 
Done! 0.32 s

Number of sentences:          77
Number of features:           32856
Freq:                         1
eta:                          0.0001
C(sigma^2):                   10

iter=0 terr=0.7494725738 serr=1 obj=2082.968899 diff=1
iter=1 terr=0.1671940928 serr=0.8831168831 obj=1406.329356 diff=0.3248438053
iter=2 terr=0.1503164557 serr=0.8831168831 obj=626.9159973 diff=0.5542182244

There are two major parameters to control the training condition

Here is the example where these two parameters are used.

% crf_learn -f 3 -c 1.5 template_file train_file model_file

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 have 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:

% crf_test -m model test.data
Rockwell        NNP     B       B
International   NNP     I       I
Corp.   NNP     I       I
's      POS     B       B
Tulsa   NNP     I       I
unit    NN      I       I
..

The last column is given (estimated) tag. If the 3rd column is true answer tag , you can evaluate the accuracy by simply seeing the difference between the 3rd and 4th columns.

Tips

Case studies

In the example directories, you can find three case studies, baseNP chunking, Text Chunking, and Japanese named entity recognition, to use CRF++.

In each directory, please try the following commands 

 % crf_learn template train model
 % crf_test  -m model test

To Do

References

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taku@chasen.org