Dutch 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", "nl") \
        .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("Behalve dat hij de koning van het noorden is, is John Snow een Engelse arts en een leider in de ontwikkeling van anesthesie en medische hygiëne.")
...
val lemmatizer = LemmatizerModel.pretrained("lemma", "nl")
        .setInputCols(Array("token"))
        .setOutputCol("lemma")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, lemmatizer))
val result = pipeline.fit(Seq.empty["Behalve dat hij de koning van het noorden is, is John Snow een Engelse arts en een leider in de ontwikkeling van anesthesie en medische hygiëne."].toDS.toDF("text")).transform(data)
import nlu

text = ["""Behalve dat hij de koning van het noorden is, is John Snow een Engelse arts en een leider in de ontwikkeling van anesthesie en medische hygiëne."""]
lemma_df = nlu.load('nl.lemma').predict(text, output_level='token')
lemma_df.lemma.values[0]

Results

[Row(annotatorType='token', begin=0, end=6, result='behalve', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=8, end=10, result='dat', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=12, end=14, result='hij', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=16, end=17, result='de', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=19, end=24, result='koning', metadata={'sentence': '0'}, embeddings=[]),
...]

Model Information

Model Name: lemma
Type: lemmatizer
Compatibility: Spark NLP 2.5.0+
Edition: Official
Input labels: [token]
Output labels: [lemma]
Language: nl
Case sensitive: false
License: Open Source

Data Source

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