Swedish 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", "sv") \
        .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("Förutom att vara kungen i norr är John Snow en engelsk läkare och en ledare inom utveckling av anestesi och medicinsk hygien.")

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

Results

[Row(annotatorType='token', begin=0, end=6, result='Förutom', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=8, end=10, result='att', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=12, end=15, result='vara', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=17, end=22, result='kung', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=24, end=24, result='i', 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: sv
Case sensitive: false
License: Open Source

Data Source

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