Chinese Lemmatizer

Description

This model uses context and language knowledge to assign all forms and inflections of a word to a single root. This enables the pipeline to treat the past and present tense of a verb, for example, as the same word instead of two completely different words. The lemmatizer takes into consideration the context surrounding a word to determine which root is correct when the word form alone is ambiguous.

Live Demo Open in Colab Download Copy S3 URI

How to use

...
lemmatizer = LemmatizerModel.pretrained("lemma", "zh") \
        .setInputCols(["token"]) \
        .setOutputCol("lemma")
nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, lemmatizer])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
results = light_pipeline.fullAnnotate("除了担任北方国王之外，约翰·斯诺（John Snow）是一位英国医师，也是麻醉和医疗卫生发展的领导者。")

...
val lemmatizer = LemmatizerModel.pretrained("lemma", "zh")
        .setInputCols(Array("token"))
        .setOutputCol("lemma")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, lemmatizer))
val data = Seq("除了担任北方国王之外，约翰·斯诺（John Snow）是一位英国医师，也是麻醉和医疗卫生发展的领导者。").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu

text = ["""除了担任北方国王之外，约翰·斯诺（John Snow）是一位英国医师，也是麻醉和医疗卫生发展的领导者。"""]
lemma_df = nlu.load('zh.lemma').predict(text, output_level='document')
lemma_df.lemma.values[0]

Results

[Row(annotatorType='token', begin=0, end=20, result='除了担任北方国王之外，约翰·斯诺（John', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=22, end=50, result='Snow）是一位英国医师，也是麻醉和医疗卫生发展的领导者。', metadata={'sentence': '0'}, embeddings=[]),
...]

Model Information

Data Source

The model is imported from https://universaldependencies.org