Traditional 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. This model was trained on traditional characters in Chinese texts.

Reference:

• 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 for the Tokenizer stage.

document_assembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")
    
sentence_detector = SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

word_segmenter = WordSegmenterModel.pretrained("wordseg_gsd_ud_trad", "zh")\
        .setInputCols(["sentence"])\
        .setOutputCol("token")    
pipeline = Pipeline(stages=[document_assembler,sentence_detector, word_segmenter])
ws_model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
example = spark.createDataFrame([['然而，這樣的處理也衍生了一些問題。']], ["text"])
result = ws_model.transform(example)

val document_assembler = DocumentAssembler()
        .setInputCol("text")
        .setOutputCol("document")
        
val sentence_detector = SentenceDetector()
    .setInputCols("document")
    .setOutputCol("sentence")

val word_segmenter = WordSegmenterModel.pretrained("wordseg_gsd_ud_trad", "zh")
        .setInputCols(Array("sentence"))
        .setOutputCol("token")
val pipeline = new Pipeline().setStages(Array(document_assembler,sentence_detector, word_segmenter))
val data = Seq("然而，這樣的處理也衍生了一些問題。").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu

text = ["""然而，這樣的處理也衍生了一些問題。"""]
token_df = nlu.load('zh.segment_words.gsd').predict(text)
token_df

Results

+-----------------------------------------+-----------------------------------------------------------+
|text                                     | result                                                    |
+-----------------------------------------+-----------------------------------------------------------+
|然而 ， 這樣 的 處理 也 衍生 了 一些 問題 。 |[然而, ，, 這樣, 的, 處理, 也, 衍生, 了, 一些, 問題, 。]      |
+-----------------------------------------+-----------------------------------------------------------+

Model Information

Data Source

The model was trained on the Universal Dependencies for Traditional Chinese annotated and converted by Google.

Benchmarking

| precision    | recall   | f1-score   |
|--------------|----------|------------|
| 0.7392       | 0.7754   | 0.7569     |