Stop Words Cleaner for Turkish

Description

This model removes ‘stop words’ from text. Stop words are words so common that they can be removed without significantly altering the meaning of a text. Removing stop words is useful when one wants to deal with only the most semantically important words in a text, and ignore words that are rarely semantically relevant, such as articles and prepositions.

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How to use

...
stop_words = StopWordsCleaner.pretrained("stopwords_tr", "tr") \
        .setInputCols(["token"]) \
        .setOutputCol("cleanTokens")
nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, stop_words])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
results = light_pipeline.fullAnnotate("John Snow, kuzeyin kralı olmanın yanı sıra bir İngiliz doktordur ve anestezi ve tıbbi hijyenin geliştirilmesinde liderdir.")
...
val stopWords = StopWordsCleaner.pretrained("stopwords_tr", "tr")
        .setInputCols(Array("token"))
        .setOutputCol("cleanTokens")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, stopWords))
val result = pipeline.fit(Seq.empty["John Snow, kuzeyin kralı olmanın yanı sıra bir İngiliz doktordur ve anestezi ve tıbbi hijyenin geliştirilmesinde liderdir."].toDS.toDF("text")).transform(data)
import nlu

text = ["""John Snow, kuzeyin kralı olmanın yanı sıra bir İngiliz doktordur ve anestezi ve tıbbi hijyenin geliştirilmesinde liderdir."""]
stopword_df = nlu.load('tr.stopwords').predict(text)
stopword_df[['cleanTokens']]

Results

[Row(annotatorType='token', begin=0, end=3, result='John', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=5, end=8, result='Snow', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=9, end=9, result=',', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=11, end=17, result='kuzeyin', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=19, end=23, result='kralı', metadata={'sentence': '0'}),
...]

Model Information

Model Name: stopwords_tr
Type: stopwords
Compatibility: Spark NLP 2.5.4+
Edition: Official
Input Labels: [token]
Output Labels: [cleanTokens]
Language: tr
Case sensitive: false
License: Open Source

Data Source

The model is imported from https://github.com/WorldBrain/remove-stopwords