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.

References:

• 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 Copy S3 URI

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_weibo', 'zh')\
        .setInputCols("document")\
        .setOutputCol("token")      
pipeline = Pipeline(stages=[document_assembler, word_segmenter])
model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
example = spark.createDataFrame([['然而，这样的处理也衍生了一些问题。']], ["text"])
result = model.transform(example)

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

val word_segmenter = WordSegmenterModel.pretrained("wordseg_weibo", "zh")
        .setInputCols("document")
        .setOutputCol("token")
val pipeline = new Pipeline().setStages(Array(document_assembler, word_segmenter))
val data = Seq("然而，这样的处理也衍生了一些问题。").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu

text = ["""然而，这样的处理也衍生了一些问题。"""]
token_df = nlu.load('zh.segment_words.weibo').predict(text, output_level='token')
token_df

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

The model is trained on the data set from He, Hangfeng, and Xu Sun. “F-score driven max margin neural network for named entity recognition in chinese social media.” arXiv preprint arXiv:1611.04234 (2016).

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  |