code: def noun_phrase_extraction(docs, parser):
      """
      Extract noun-phrases from a textual corpus
  
      Args:
          docs (List[String]): A list of documents
          parser (SpacyInstance): Spacy NLP parser
      Returns:
          List of topics with their tf_idf, c_value, language-model scores
      """
      logger.info("extracting NPs")
      np_app = NPScorer(parser=parser)
      np_result = np_app.score_documents(docs, return_all=True)
      logger.info(
          "got NP extractor result. %s phrases were extracted",
          str(len(np_result)),
      )
      return np_result
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import argparse
  import csv
  import logging
  import os
  import sys
  from multiprocessing import Pool
  from os import makedirs, path
  from nlp_architect import LIBRARY_OUT
  from nlp_architect.utils.embedding import FasttextEmbeddingsModel
  from nlp_architect.utils.io import validate_existing_directory
  from nlp_architect.utils.text import SpacyInstance
  from .np_scorer import NPScorer
label_desc: Use image node_id 1 for calling a global function with example usage: noun_phrase_extraction(docs, parser) and returns: np_result