load corresponding parser

gargantext/util/toolchain/ngrams_extraction.py

@@ -50,12 +50,12 @@ def extract_ngrams(corpus, keys=('title', 'abstract', ), do_subngrams = DEFAULT_
         resource_type_index = corpus.resources()[0]['type']
         documents_count = 0
         resource_type = RESOURCETYPES[resource_type_index]
-        default_language_iso2 = resource_type['default_language']
+        default_language_iso2 = resource_type['default_languages']
         for documents_count, document in enumerate(corpus.children('DOCUMENT')):
             # get ngrams extractor for the current document
             language_iso2 = document.hyperdata.get('language_iso2', default_language_iso2)
             try:
-                # this looks for a parser in constants.LANGUAGES
+                # this looks for a tagger in constants.LANGUAGES
                 ngramsextractor = ngramsextractors[language_iso2]
             except KeyError:
                 # skip document

gargantext/util/toolchain/parsing.py

 from gargantext.util.db import *
 from gargantext.models import *
 from gargantext.constants import *
-
+from gargantext.util.parsers import *
 from collections import defaultdict
 from re          import sub
 
@@ -19,7 +19,9 @@ def parse(corpus):
             # information about the resource
             if resource['extracted']:
                 continue
-            resource_parser = RESOURCETYPES[resource['type']]['parser']
+            #source store available module for a resource
+            source = get_resource(resource["type"])
+            resource_parser = load_parser(source)
             resource_path = resource['path']
             # extract and insert documents from corpus resource into database
             for hyperdata in resource_parser(resource_path):