class_docstr: Yake is an Unsupervised, Corpus-Independent, Domain and
  Language-Independent and Single-Document keyword extraction algorithm.
  
  Extracting keywords from texts has become a challenge for individuals and
  organizations as the information grows in complexity and size. The need to
  automate this task so that text can be processed in a timely and adequate
  manner has led to the emergence of automatic keyword extraction tools. Yake
  is a novel feature-based system for multi-lingual keyword extraction, which
  supports texts of different sizes, domain or languages. Unlike other
  approaches, Yake does not rely on dictionaries nor thesauri, neither is
  trained against any corpora. Instead, it follows an unsupervised approach
  which builds upon features extracted from the text, making it thus
  applicable to documents written in different languages without the need for
  further knowledge. This can be beneficial for a large number of tasks and a
  plethora of situations where access to training corpora is either limited or
  restricted. The algorithm makes use of the position of a sentence and token.
  Therefore, to use the annotator, the text should be first sent through a
  Sentence Boundary Detector and then a tokenizer.
  
  See the parameters section for tweakable parameters to get the best result
  from the annotator.
  
  Note that each keyword will be given a keyword score greater than 0 (The
  lower the score better the keyword). Therefore to filter the keywords, an
  upper bound for the score can be set with :meth:`.setThreshold`.
  
  For extended examples of usage, see the `Examples
  <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/keyword-extraction/Keyword_Extraction_YAKE.ipynb>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``TOKEN``              ``CHUNK``
  ====================== ======================
  
  Parameters
  ----------
  minNGrams
      Minimum N-grams a keyword should have, by default 2
  maxNGrams
      Maximum N-grams a keyword should have, by default 3
  threshold
      Keyword Score threshold, by default -1
  windowSize
      Window size for Co-Occurrence, by default 3
  nKeywords
      Number of Keywords to extract, by default 30
  stopWords
      the words to be filtered out, by default english stop words from Spark
      ML
  
  References
  ----------
  `Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C.
  and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using
  Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509,
  pp 257-289
  <https://www.sciencedirect.com/science/article/pii/S0020025519308588>`__
  
  **Paper abstract:**
  
  *As the amount of generated information grows, reading and summarizing texts
  of large collections turns into a challenging task. Many documents do not
  come with descriptive terms, thus requiring humans to generate keywords
  on-the-fly. The need to automate this kind of task demands the development
  of keyword extraction systems with the ability to automatically identify
  keywords within the text. One approach is to resort to machine-learning
  algorithms. These, however, depend on large annotated text corpora, which
  are not always available. An alternative solution is to consider an
  unsupervised approach. In this article, we describe YAKE!, a light-weight
  unsupervised automatic keyword extraction method which rests on statistical
  text features extracted from single documents to select the most relevant
  keywords of a text. Our system does not need to be trained on a particular
  set of documents, nor does it depend on dictionaries, external corpora, text
  size, language, or domain. To demonstrate the merits and significance of
  YAKE!, we compare it against ten state-of-the-art unsupervised approaches
  and one supervised method. Experimental results carried out on top of twenty
  datasets show that YAKE! significantly outperforms other unsupervised
  methods on texts of different sizes, languages, and domains.*
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> sentenceDetector = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentence")
  >>> token = Tokenizer() \
  ...     .setInputCols(["sentence"]) \
  ...     .setOutputCol("token") \
  ...     .setContextChars(["(", "]", "?", "!", ".", ","])
  >>> keywords = YakeKeywordExtraction() \
  ...     .setInputCols(["token"]) \
  ...     .setOutputCol("keywords") \
  ...     .setThreshold(0.6) \
  ...     .setMinNGrams(2) \
  ...     .setNKeywords(10)
  >>> pipeline = Pipeline().setStages([
  ...     documentAssembler,
  ...     sentenceDetector,
  ...     token,
  ...     keywords
  ... ])
  >>> data = spark.createDataFrame([[
  ...     "Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague, but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom  and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, it will get increased mindshare in this community, too (though it already has plenty of that thanks to Tensorflow and other projects). Kaggle has a bit of a history with Google, too, but that's pretty recent. Earlier this month, Google and Kaggle teamed up to host a $100,000 machine learning competition around classifying YouTube videos. That competition had some deep integrations with the Google Cloud Platform, too. Our understanding is that Google will keep the service running - likely under its current name. While the acquisition is probably more about Kaggle's community than technology, Kaggle did build some interesting tools for hosting its competition and 'kernels', too. On Kaggle, kernels are basically the source code for analyzing data sets and developers can share this code on the platform (the company previously called them 'scripts'). Like similar competition-centric sites, Kaggle also runs a job board, too. It's unclear what Google will do with that part of the service. According to Crunchbase, Kaggle raised $12.5 million (though PitchBook says it's $12.75) since its   launch in 2010. Investors in Kaggle include Index Ventures, SV Angel, Max Levchin, NaRavikant, Google chie economist Hal Varian, Khosla Ventures and Yuri Milner"
  ... ]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  
  Combine the result and score (contained in keywords.metadata)
  
  >>> scores = result \
  ...     .selectExpr("explode(arrays_zip(keywords.result, keywords.metadata)) as resultTuples") \
  ...     .selectExpr("resultTuples['0'] as keyword", "resultTuples['1'].score as score")
  
  Order ascending, as lower scores means higher importance
  
  >>> scores.orderBy("score").show(5, truncate = False)
  +---------------------+-------------------+
  |keyword              |score              |
  +---------------------+-------------------+
  |google cloud         |0.32051516486864573|
  |google cloud platform|0.37786450577630676|
  |ceo anthony goldbloom|0.39922830978423146|
  |san francisco        |0.40224744669493756|
  |anthony goldbloom    |0.41584827825302534|
  +---------------------+-------------------+
docstr: Sets window size for Co-Occurrence, by default 3.
  
  Parameters
  ----------
  value : int
      Window size for Co-Occurrence
code: def setWindowSize(self, value):
      """Sets window size for Co-Occurrence, by default 3.
  
      Parameters
      ----------
      value : int
          Window size for Co-Occurrence
      """
      return self._set(windowSize=value)
  
file_path: spark-nlp/python/sparknlp/annotator/keyword_extraction/yake_keyword_extraction.py
imports: from sparknlp.common import *
label_desc: Use image node_id 2 for calling the YakeKeywordExtraction obj's underlying member method code with example usage: obj.setWindowSize(value) and returns: self