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  • John Snow Labs
  • Jul 29, 2020

Latin Lemmatizer

  • lemmatizer
  • la

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", "la") \
        .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("Alius est esse regem Aquilonis, et de Anglis medicus et nives Ioannes dux in progressus medicinae anesthesia et hygiene.")

...
val lemmatizer = LemmatizerModel.pretrained("lemma", "la")
        .setInputCols(Array("token"))
        .setOutputCol("lemma")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, lemmatizer))
val data = Seq("Alius est esse regem Aquilonis, et de Anglis medicus et nives Ioannes dux in progressus medicinae anesthesia et hygiene.").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu

text = ["""Alius est esse regem Aquilonis, et de Anglis medicus et nives Ioannes dux in progressus medicinae anesthesia et hygiene."""]
lemma_df = nlu.load('la.lemma').predict(text, output_level='document')
lemma_df.lemma.values[0]

Results

[Row(annotatorType='token', begin=0, end=4, result='Alius', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=6, end=8, result='sum', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=10, end=13, result='sum', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=15, end=19, result='regem', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=21, end=29, result='Aquilonis', 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: la
Case sensitive: false
License: Open Source

Data Source

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