Persian 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", "fa") \
.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(['فرماندهان	فرماندهی	فرمانده	فرمانده‌ای	فرماندهٔ	فرماند'])

...
val lemmatizer = LemmatizerModel.pretrained("lemma", "fa")
.setInputCols(Array("token"))
.setOutputCol("lemma")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, lemmatizer))
val data = Seq("فرماندهان	فرماندهی	فرمانده	فرمانده‌ای	فرماندهٔ	فرماند").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu

text = ["""فرماندهان	فرماندهی	فرمانده	فرمانده‌ای	فرماندهٔ	فرماند"""]
lemma_df = nlu.load('fa.lemma').predict(text)
lemma_df.lemma.values[0]

Results

{'lemma': [Annotation(token, 0, 8, فرمانده, {'sentence': '0'}),
Annotation(token, 10, 17, فرماندهی, {'sentence': '0'}),
Annotation(token, 19, 25, فرمانده, {'sentence': '0'}),
Annotation(token, 27, 36, فرمانده, {'sentence': '0'}),
Annotation(token, 38, 45, فرمانده, {'sentence': '0'}),
Annotation(token, 47, 52, فرماند, {'sentence': '0'})]}

Model Information

Data Source

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