How to compile

Suppose that ZPar has been downloaded to the directory zpar. To make the joint Chinese word segmentor and POS tagger, type make chinese.postagger. This will create a directory zpar/dist/chinese.postagger, in which there are two files: train and tagger. The file train is used to train a joint model of Chinese word segmentation and POS tagging,and the file tagger is used to segment and assign POS tags to new texts using a trained joint model.

Format of inputs and outputs

The input files to the tagger are formatted as a sequence of Chinese characters. An example input is:


The output files contain space-separated words:

 ZPar_NN 可以_VV 分析_VV 中文_NN 和_CC 英文_NN

The output format is also the format of training files for the train executable.

Both input and output files must be enemd in utf8. Here is a script that transfers files that are enemd in gb to the utf8 encoding.

How to train a model

To train a model, use

 zpar/dist/chinese.postagger/train <train-file> <model-file> <number of iterations>

For example, using the example train file, you can train a model by

 zpar/dist/chinese.postagger/train train.txt model 1

After training is completed, a new file model will be created in the current directory, which can be used to do joint segmentation and POS taging to Chinese. The above command performs training with one iteration (see How to tune the performance of a system) using the training file.

How to segment and POS-tag new texts

To apply an existing model to do joint segmentation and POS tagging to new texts, use

 zpar/dist/chinese.postagger/tagger <model> [<input-file>] [<output-file>]

where the input file and output file are optional. If the output file is not specified, segmented and POS-tagged texts will be printed to the console. If the input file is not specified, raw texts will be read from the console. For example, using the model we just trained, we can segment and POS-tag an example input by

 zpar/dist/chinese.postagger/tagger model input.txt output.txt

The output file contains automatically segmented and POS-tagged texts.

Outputs and evaluation

Automatically segmented and POS-tagged texts contain errors. In order to evaluate the quality of the outputs, we can manually specify the segmentation and POS tags of a sample, and compare the outputs with the correct sample.

A manually specified segmentation and POS tagging of the input file is given in this example reference file. Here is a Python script that performs automatic evaluation.

Using the above output.txt and reference.txt, we can evaluate the accuracies by typing

 python output.txt reference.txt

You can find the precision, recall, and f-score here. See the explanation of these measures on Wikipedia.

How to tune the performance of a system

The performance of the system after one training iteration may not be optimal. You can try training a model for another few iterations, after each you compare the performance. You can choose the model that gives the highest f-score on your test data. We conventionally call this test file the development test data, because you develop a joint segmentation and POS tagging model using this. Here is a a shell script that automatically trains the joint segmentor and POS tagger for 30 iterations, and after the ith iteration, stores the model file to model.i. You can compare the f-score of all 30 iterations and choose model.k, which gives the best f-score, as the final model. In this file, there is a variable called zpar. You need to set this variable to the relative directory of zpar/dist/chinese.postagger.

Source code

The source code for the joint segmentor and POS tagger can be found at


where CHINESE_TAGGER_IMPL is a macro defined in Makefile, and specifies a specific implementation for the joint segmentor and POS tagger.

The Chinese POS-tagger by default performs segmentation and tagging simultaneously. This means that if the input sentence has been segmented, the system will resegment the sentence. There is one implementation that performs POS-tagging on segmented sentences. The name of the implementation is segmented, and you can compile this system by setting CHINESE_TAGGER_IMPL to segmented in Makefile. The compilation, training, and usage are the same as the other taggers.