class_docstr: Regular expression tagger, inheriting from
  ``SequentialBackoffLemmatizer`` and ``RegexpTagger``.
class_docstr_tok: ["Regular","expression","tagger",",","inheriting","from","``","SequentialBackoffLemmatizer","``","and","``","RegexpTagger","``","."]
docstr: Setup for RegexpLemmatizer()
  :type regexps: list
  :param regexps: List of tuples of form (PATTERN, REPLACEMENT)
  :param backoff: Next lemmatizer in backoff chain.
docstr_tok: ["Setup","for","RegexpLemmatizer","(",")",":","type","regexps",":","list",":","param","regexps",":","List","of","tuples","of","form","(","PATTERN",",","REPLACEMENT",")",":","param","backoff",":","Next","lemmatizer","in","backoff","chain","."]
code: def __init__(
      self,
      regexps=None,
      source=None,
      backoff=None,
      verbose: bool = False,
  ):
      """Setup for RegexpLemmatizer()
      :type regexps: list
      :param regexps: List of tuples of form (PATTERN, REPLACEMENT)
      :param backoff: Next lemmatizer in backoff chain.
      """
      SequentialBackoffLemmatizer.__init__(
          self, backoff=None, verbose=verbose
      )
      RegexpTagger.__init__(self, regexps, backoff)
      self._regexs = regexps
      self.source = source
  
imports: import re
  import reprlib
  from typing import Dict, List, Optional, Tuple
  from nltk.tag.sequential import RegexpTagger, SequentialBackoffTagger, TaggerI, UnigramTagger
label_desc: Use image node_id 1 to create a new RegexpLemmatizer object from inherited base classes: SequentialBackoffLemmatizer, RegexpTagger with example: obj = RegexpLemmatizer(regexps, source, backoff, verbose)