Portuguese Lemmatizer


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", "pt") \
        .setInputCols(["token"]) \
nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, lemmatizer])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
results = light_pipeline.fullAnnotate("Além de ser o rei do norte, John Snow é um médico inglês e líder no desenvolvimento de anestesia e higiene médica.")
val lemmatizer = LemmatizerModel.pretrained("lemma", "pt")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, lemmatizer))
val result = pipeline.fit(Seq.empty["Além de ser o rei do norte, John Snow é um médico inglês e líder no desenvolvimento de anestesia e higiene médica."].toDS.toDF("text")).transform(data)


[Row(annotatorType='token', begin=0, end=3, result='Além', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=5, end=6, result='de', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=8, end=10, result='ser', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=12, end=12, result='o', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=14, end=16, result='rei', 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: pt
Case sensitive: false
License: Open Source

Data Source

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