Word Segmenter for Chinese

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.

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How to use

word_segmenter = WordSegmenterModel.pretrained("wordseg_msra", "zh")        .setInputCols(["sentence"])        .setOutputCol("token")
pipeline = Pipeline(stages=[document_assembler, word_segmenter])
ws_model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
example = spark.createDataFrame([['从John Snow Labs你好！ ']], ["text"])
result = ws_model.transform(example)

val word_segmenter = WordSegmenterModel.pretrained("wordseg_msra", "zh")
        .setInputCols(Array("sentence"))
        .setOutputCol("token")
val pipeline = new Pipeline().setStages(Array(document_assembler, word_segmenter))
val data = Seq("从John Snow Labs你好！ ").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu
text = [""从John Snow Labs你好！ ""]
token_df = nlu.load('zh.segment_words.msra').predict(text)
token_df
    

Results

0     从
1     J
2     o
3     h
4     n
5     S
6     n
7     o
8     w
9     L
10    a
11    b
12    s
13    你
14    好
15    ！
Name: token, dtype: object

Model Information