Chinese Word Segmentation

Description

WordSegmenterModel (WSM) is based on maximum entropy probability model to detect word boundaries in Chinese text. Chinese text is written without white space between the words, and a computer-based application cannot know a priori which sequence of ideograms form a word. In many natural language processing tasks such as part-of-speech (POS) and named entity recognition (NER) require word segmentation as a initial step.

We trained this model on the the Chinese Penn Treebank version 9 data set following the research paper by Nianwen Xue.

References:

• Xue, Nianwen, et al. Chinese Treebank 9.0 LDC2016T13. Web Download. Philadelphia: Linguistic Data Consortium, 2016.
• Xue, Nianwen. “Chinese word segmentation as character tagging.” International Journal of Computational Linguistics & Chinese Language Processing, Volume 8, Number 1, February 2003: Special Issue on Word Formation and Chinese Language Processing. 2003.).

Live Demo Open in Colab Download

How to use

Use as part of an nlp pipeline as a substitute of the Tokenizer stage.

document_assembler = DocumentAssembler()\
        .setInputCol("text")\
        .setOutputCol("document")

word_segmenter = WordSegmenterModel.pretrained("wordseg_ctb9")\
        .setInputCols("document")\
        .setOutputCol("token")
        
pipeline = Pipeline(stages=[
        document_assembler,
        word_segmenter
        ])

ws_model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))

example = spark.createDataFrame(pd.DataFrame({'text': ["""然而，这样的处理也衍生了一些问题。"""]}))

result = ws_model.transform(example)

...

val word_segmenter = WordSegmenterModel.pretrained("wordseg_ctb9", "zh")
        .setInputCols("document")
        .setOutputCol("token")

val pipeline = new Pipeline().setStages(Array(document_assembler, word_segmenter))

val result = pipeline.fit(Seq.empty["然而，这样的处理也衍生了一些问题。"].toDS.toDF("text")).transform(data)

Results

+----------------------------------+--------------------------------------------------------+
|text                              |result                                                  |
+----------------------------------+--------------------------------------------------------+
|然而，这样的处理也衍生了一些问题。|[然而, ，, 这样, 的, 处理, 也, 衍生, 了, 一些, 问题, 。]|
+----------------------------------+--------------------------------------------------------+

Model Information

Data Source

ctb9_train.chartag

Benchmarking

| Model         | precision    | recall       | f1-score     |
|---------------|--------------|--------------|--------------|
| WORSEG_CTB    |      0,6453  |      0,6341  |      0,6397  |
| WORDSEG_WEIBO |      0,5454  |      0,5655  |      0,5553  |
| WORDSEG_MSR   |      0,5984  |      0,6088  |      0,6035  |
| WORDSEG_PKU   |      0,6094  |      0,6321  |      0,6206  |
| WORDSEG_LARGE |      0,6326  |      0,6269  |      0,6297  |