class_docstr: Spanish rule-based lemmatizer with morph-based rule selection.
class_docstr_tok: ["Spanish","rule-based","lemmatizer","with","morph-based","rule","selection","."]
docstr: Lemmatize an adverb.
  
  word (str): The word to lemmatize.
  features (List[str]): The morphological features as a list of Feat=Val
      pairs.
  index (List[str]): The POS-specific lookup list.
  
  RETURNS (List[str]): The list of lemmas.
docstr_tok: ["Lemmatize","an","adverb",".","word","(","str",")",":","The","word","to","lemmatize",".","features","(","List","[","str","]",")",":","The","morphological","features","as","a","list","of","Feat=Val","pairs",".","index","(","List","[","str","]",")",":","The","POS-specific","lookup","list",".","RETURNS","(","List","[","str","]",")",":","The","list","of","lemmas","."]
code: def lemmatize_adv(
      self, word: str, features: List[str], rule: str, index: List[str]
  ) -> List[str]:
      """
      Lemmatize an adverb.
  
      word (str): The word to lemmatize.
      features (List[str]): The morphological features as a list of Feat=Val
          pairs.
      index (List[str]): The POS-specific lookup list.
  
      RETURNS (List[str]): The list of lemmas.
      """
  
      # Apply lemmatization rules
      for old, new in self.lookups.get_table("lemma_rules").get(
          "adverbs", []
      ):
          if word == old:
              return [new]
  
      # If none of the rules applies, return the original word
      return [word]
  
file_path: spacy/spacy/lang/es/lemmatizer.py
imports: import re
  from typing import List, Optional, Tuple
  from ...pipeline import Lemmatizer
  from ...tokens import Token
label_desc: Use image node_id 5 for calling the SpanishLemmatizer obj's underlying member method code with example usage: obj.lemmatize_adv(word, features, rule, index) and returns: list, list