code: def extract_be_comp_relations(self, first_paragraph):
      be_comp = set()
      be_comp_norm = set()
      if first_paragraph:
          doc = self.spacy.parser(first_paragraph)
          for token in doc:
              target = token.text
              target_lemma = token.lemma_
              relation = token.dep_
              governor = token.head.text
              governor_lemma = token.head.lemma_
              if relation == "acl":
                  break
              if relation == "punct" and target == ".":
                  break
              elif relation == "cop":
                  be_comp.add(governor)
                  be_comp_norm.add(governor_lemma)
              elif relation == "nsubj":
                  be_comp.add(target)
                  be_comp_norm.add(target_lemma)
              elif relation == "dep":
                  be_comp.add(governor)
                  be_comp_norm.add(governor_lemma)
              elif relation == "compound":
                  be_comp.add(target + " " + governor)
                  be_comp_norm.add(target_lemma + " " + governor_lemma)
              elif relation == "amod":
                  be_comp.add(target + " " + governor)
                  be_comp_norm.add(target_lemma + " " + governor_lemma)
              elif relation in ["conj", "appos"]:
                  be_comp.add(target)
                  be_comp_norm.add(target_lemma)
  
      return be_comp, be_comp_norm
  
imports: import logging
  import os
  import re
  import sys
  from nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_page import WikipediaPage
  from nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_page_extracted_relations import WikipediaPageExtractedRelations
  from nlp_architect.data.cdc_resources.wikipedia.wiki_search_page_result import WikipediaSearchPageResult
  from nlp_architect.utils.text import SpacyInstance
label_desc: Use image node_id 11 for calling the WikiOnline obj's underlying member method code with example usage: obj.extract_be_comp_relations(first_paragraph) and returns: be_comp,  be_comp_norm