Estonain 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

Use as part of a pipeline after tokenisation.

...
lemmatizer = LemmatizerModel.pretrained("lemma", "et") \
        .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(['üheksandana üheksas üheksanda Üheksas'])

...
val lemmatizer = LemmatizerModel.pretrained("lemma", "et")
        .setInputCols(Array("token"))
        .setOutputCol("lemma")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, lemmatizer))
val data = Seq("üheksandana üheksas üheksanda Üheksas").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu

text = ["üheksandana üheksas üheksanda Üheksas"]
lemma_df = nlu.load('et.lemma').predict(text, output_level='document')
lemma_df.lemma.values[0]

Results

{'lemma': [Annotation(token, 0, 10, üheksas, {'sentence': '0'}),
   Annotation(token, 12, 18, üheksas, {'sentence': '0'}),
   Annotation(token, 20, 28, üheksas, {'sentence': '0'}),
   Annotation(token, 30, 36, üheksas, {'sentence': '0'})]}

Model Information

Data Source

This model is trained on data obtained from https://universaldependencies.org/