class_docstr: Compute words embedding features for mentions
code: def normalize_word(w):
      if w is None:
          return MISSING_WORD
      return re.sub(r"\d", "0", w.lower_)
  
imports: import re
  import io
  from six import string_types, integer_types
  from spacy.tokens import Span, Token
  from neuralcoref.train.compat import unicode_
  from neuralcoref.train.utils import encode_distance, parallel_process
  import spacy
  import numpy
label_desc: Use image node_id 3 for calling the EmbeddingExtractor obj's underlying member method code with example usage: obj.normalize_word(w) and returns: re, MISSING_WORD