Traditional Chinese Word Segmentation


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.


  • 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.).


How to use

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

word_segmenter = WordSegmenterModel.pretrained("wordseg_gsd_ud_trad", "zh")\
pipeline = Pipeline(stages=[document_assembler, word_segmenter])
ws_model =[[""]]).toDF("text"))
example = spark.createDataFrame(pd.DataFrame({'text': ["""然而,這樣的處理也衍生了一些問題。"""]}))
result = ws_model.transform(example)
val word_segmenter = WordSegmenterModel.pretrained("wordseg_gsd_ud_trad", "zh")
val pipeline = new Pipeline().setStages(Array(document_assembler, word_segmenter))
val result =["然而這樣的處理也衍生了一些問題。"].toDS.toDF("text")).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)


|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

Model Name: wordseg_gsd_ud_trad
Compatibility: Spark NLP 2.7.0+
License: Open Source
Edition: Official
Input Labels: [document]
Output Labels: [token]
Language: zh

Data Source

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


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