docs: def vectorize(self, docs):
          """
          Vectorizes a list of documents using their DCS representations.
          """
          doc_core_sems, all_concepts = self._extract_core_semantics(docs)
  
          shape = (len(docs), len(all_concepts))
          vecs = np.zeros(shape)
          for i, core_sems in enumerate(doc_core_sems):
              for con, weight in core_sems:
                  j = all_concepts.index(con)
                  vecs[i,j] = weight
  
          # Normalize
          return vecs/np.max(vecs)