Type Members

1.  class ChunkTokenizer extends Tokenizer

2.  class ChunkTokenizerModel extends TokenizerModel

3.  class Chunker extends AnnotatorModel[Chunker] with HasSimpleAnnotate[Chunker]

   This annotator matches a pattern of part-of-speech tags in order to return meaningful phrases from document

   This annotator matches a pattern of part-of-speech tags in order to return meaningful phrases from document

   See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/ChunkerTestSpec.scala for reference on how to use this API.

4.  class DateMatcher extends AnnotatorModel[DateMatcher] with HasSimpleAnnotate[DateMatcher] with DateMatcherUtils

   Matches standard date formats into a provided format Reads from different forms of date and time expressions and converts them to a provided date format.

   Matches standard date formats into a provided format Reads from different forms of date and time expressions and converts them to a provided date format. Extracts only ONE date per sentence. Use with sentence detector for more matches.

   Reads the following kind of dates:

   1978-01-28, 1984/04/02,1/02/1980, 2/28/79, The 31st of April in the year 2008, "Fri, 21 Nov 1997" , "Jan 21, ‘97" , Sun, Nov 21, jan 1st, next thursday, last wednesday, today, tomorrow, yesterday, next week, next month, next year, day after, the day before, 0600h, 06:00 hours, 6pm, 5:30 a.m., at 5, 12:59, 23:59, 1988/11/23 6pm, next week at 7.30, 5 am tomorrow

   See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/DateMatcherTestSpec.scala for further reference on how to use this API

5.  trait DateMatcherUtils extends Params
6.  class DocumentNormalizer extends AnnotatorModel[DocumentNormalizer] with HasSimpleAnnotate[DocumentNormalizer]

   Annotator which normalizes raw text from tagged text, e.g.

   Annotator which normalizes raw text from tagged text, e.g. scraped web pages or xml documents, from document type columns into Sentence. Removes all dirty characters from text following one or more input regex patterns. Can apply not wanted character removal with a specific policy. Can apply lower case normalization.

   See DocumentNormalizer test class for examples examples of usage.

7.  class Lemmatizer extends AnnotatorApproach[LemmatizerModel]

   Class to find standarized lemmas from words.

   Class to find standarized lemmas from words. Uses a user-provided or default dictionary.

   Retrieves lemmas out of words with the objective of returning a base dictionary word. Retrieves the significant part of a word.

   lemmaDict: A dictionary of predefined lemmas must be provided

   See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/LemmatizerTestSpec.scala for examples of how to use this API

8.  class LemmatizerModel extends AnnotatorModel[LemmatizerModel] with HasSimpleAnnotate[LemmatizerModel]

   Class to find standarized lemmas from words.

   Class to find standarized lemmas from words. Uses a user-provided or default dictionary.

   Retrieves lemmas out of words with the objective of returning a base dictionary word. Retrieves the significant part of a word

   See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/LemmatizerTestSpec.scala for examples of how to use this API

9.  class MultiDateMatcher extends AnnotatorModel[MultiDateMatcher] with HasSimpleAnnotate[MultiDateMatcher] with DateMatcherUtils

   Matches standard date formats into a provided format

10.  class NGramGenerator extends AnnotatorModel[NGramGenerator] with HasSimpleAnnotate[NGramGenerator]

    A feature transformer that converts the input array of strings (annotatorType TOKEN) into an array of n-grams (annotatorType CHUNK).

    A feature transformer that converts the input array of strings (annotatorType TOKEN) into an array of n-grams (annotatorType CHUNK). Null values in the input array are ignored. It returns an array of n-grams where each n-gram is represented by a space-separated string of words.

    When the input is empty, an empty array is returned. When the input array length is less than n (number of elements per n-gram), no n-grams are returned.

    See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/NGramGeneratorTestSpec.scala for reference on how to use this API.

11.  class Normalizer extends AnnotatorApproach[NormalizerModel]

    Annotator that cleans out tokens.

    Annotator that cleans out tokens. Requires stems, hence tokens. Removes all dirty characters from text following a regex pattern and transforms words based on a provided dictionary

    See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/NormalizerTestSpec.scala for examples on how to use the API

12.  class NormalizerModel extends AnnotatorModel[NormalizerModel] with HasSimpleAnnotate[NormalizerModel]

    Annotator that cleans out tokens.

    Annotator that cleans out tokens. Requires stems, hence tokens.

    Removes all dirty characters from text following a regex pattern and transforms words based on a provided dictionary

    See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/NormalizerTestSpec.scala for examples on how to use the API

13.  trait ReadablePretrainedLemmatizer extends ParamsAndFeaturesReadable[LemmatizerModel] with HasPretrained[LemmatizerModel]
14.  trait ReadablePretrainedStopWordsCleanerModel extends ParamsAndFeaturesReadable[StopWordsCleaner] with HasPretrained[StopWordsCleaner]
15.  trait ReadablePretrainedTextMatcher extends ParamsAndFeaturesReadable[TextMatcherModel] with HasPretrained[TextMatcherModel]
16.  trait ReadablePretrainedTokenizer extends ParamsAndFeaturesReadable[TokenizerModel] with HasPretrained[TokenizerModel]
17.  class RecursiveTokenizer extends AnnotatorApproach[RecursiveTokenizerModel] with ParamsAndFeaturesWritable

18.  class RecursiveTokenizerModel extends AnnotatorModel[RecursiveTokenizerModel] with HasSimpleAnnotate[RecursiveTokenizerModel] with ParamsAndFeaturesWritable

19.  class RegexMatcher extends AnnotatorApproach[RegexMatcherModel]

    Uses a reference file to match a set of regular expressions and put them inside a provided key.

    Uses a reference file to match a set of regular expressions and put them inside a provided key. File must be comma separated.

    Matches regular expressions and maps them to specified values optionally provided

    Rules are provided from external source file

20.  class RegexMatcherModel extends AnnotatorModel[RegexMatcherModel] with HasSimpleAnnotate[RegexMatcherModel]

    Matches regular expressions and maps them to specified values optionally provided Rules are provided from external source file

21.  class RegexTokenizer extends AnnotatorModel[RegexTokenizer] with HasSimpleAnnotate[RegexTokenizer]

    A tokenizer that splits text by regex pattern.

    A tokenizer that splits text by regex pattern.

    See also

    RegexTokenizer

22.  class Stemmer extends AnnotatorModel[Stemmer] with HasSimpleAnnotate[Stemmer]

    Hard stemming of words for cut-of into standard word references.

    Hard stemming of words for cut-of into standard word references. See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/StemmerTestSpec.scala for examples of how to use this API

23.  class StopWordsCleaner extends AnnotatorModel[StopWordsCleaner] with HasSimpleAnnotate[StopWordsCleaner]

    This annotator excludes from a sequence of strings (e.g.

    This annotator excludes from a sequence of strings (e.g. the output of a Tokenizer, Normalizer, Lemmatizer, and Stemmer) and drops all the stop words from the input sequences.

    See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/StopWordsCleanerTestSpec.scala for example of how to use this API.

24.  class TextMatcher extends AnnotatorApproach[TextMatcherModel] with ParamsAndFeaturesWritable

    Annotator to match entire phrases (by token) provided in a file against a Document

    Annotator to match entire phrases (by token) provided in a file against a Document

    See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/TextMatcherTestSpec.scala for reference on how to use this API

25.  class TextMatcherModel extends AnnotatorModel[TextMatcherModel] with HasSimpleAnnotate[TextMatcherModel]

    Extracts entities out of provided phrases

26.  class Token2Chunk extends AnnotatorModel[Token2Chunk] with HasSimpleAnnotate[Token2Chunk]

27.  class Tokenizer extends AnnotatorApproach[TokenizerModel]

    Tokenizes raw text in document type columns into TokenizedSentence .

    Tokenizes raw text in document type columns into TokenizedSentence .

    This class represents a non fitted tokenizer. Fitting it will cause the internal RuleFactory to construct the rules for tokenizing from the input configuration.

    Identifies tokens with tokenization open standards. A few rules will help customizing it if defaults do not fit user needs.

    See Tokenizer test class for examples examples of usage.

28.  class TokenizerModel extends AnnotatorModel[TokenizerModel] with HasSimpleAnnotate[TokenizerModel]

    Tokenizes raw text into word pieces, tokens.

    Tokenizes raw text into word pieces, tokens. Identifies tokens with tokenization open standards. A few rules will help customizing it if defaults do not fit user needs.

    This class represents an already fitted Tokenizer model.

    See Tokenizer test class for examples examples of usage.