code: def get_phrases(doc: Doc) -> List[Span]:
      """Compile a list of sense2vec phrases based on a processed Doc: named
      entities and noun chunks without determiners.
  
      doc (Doc): The Doc to get phrases from.
      RETURNS (list): The phrases as a list of Span objects.
      """
      spans = list(doc.ents)
      ent_words: Set[str] = set()
      for span in spans:
          ent_words.update(token.i for token in span)
      for np in get_noun_phrases(doc):
          # Prefer entities over noun chunks if there's overlap
          if not any(w.i in ent_words for w in np):
              spans.append(np)
      return spans
  
file_path: sense2vec/sense2vec/util.py
imports: from typing import Union, List, Tuple, Set
  import re
  from spacy.tokens import Doc, Token, Span
  from spacy.util import filter_spans
  from thinc.api import get_array_module
  import catalogue
label_desc: Use image node_id 6 for calling a global function with example usage: get_phrases(doc) and returns: spans