Indonesian 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.

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How to use


lemmatizer = LemmatizerModel.pretrained("lemma", "id") \
        .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("Selain menjadi raja utara, John Snow adalah seorang dokter Inggris dan pemimpin dalam pengembangan anestesi dan kebersihan medis.")

val lemmatizer = LemmatizerModel.pretrained("lemma", "id")
        .setInputCols(Array("token"))
        .setOutputCol("lemma")

Results

[Row(annotatorType='token', begin=0, end=5, result='selain', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=7, end=13, result='menjadi', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=15, end=18, result='raja', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=20, end=24, result='utara', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=25, end=25, result=',', metadata={'sentence': '0'}, embeddings=[]),
...]

Model Information

Model Name: lemma
Type: lemmatizer
Compatibility: Spark NLP 2.5.5+
Edition: Official
Input labels: [token]
Output labels: [lemma]
Language: id
Case sensitive: false
License: Open Source

Data Source

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