diff --git a/bin/init_model.py b/bin/init_model.py
index e4f5225d7..4a2811256 100644
--- a/bin/init_model.py
+++ b/bin/init_model.py
@@ -28,7 +28,7 @@ from spacy.en.lemmatizer import Lemmatizer
 from spacy.vocab import Vocab
 from spacy.vocab import write_binary_vectors
 
-from spacy.parts_of_speech import NOUN, VERB, ADJ
+from spacy.parts_of_speech import NOUN, VERB, ADJ, ADV
 
 import spacy.senses
 
@@ -80,19 +80,14 @@ def _read_probs(loc):
 
 def _read_senses(loc):
     lexicon = defaultdict(lambda: defaultdict(list))
-    sense_names = dict((s, i) for i, s in enumerate(spacy.senses.STRINGS))
-    pos_ids = {'noun': NOUN, 'verb': VERB, 'adjective': ADJ}
+    pos_tags = [None, NOUN, VERB, ADJ, ADV, None]
     for line in codecs.open(str(loc), 'r', 'utf8'):
-        sense_strings = line.split()
-        word = sense_strings.pop(0)
-        for sense in sense_strings:
-            pos, sense = sense[3:].split('.')
-            if pos[0].upper() == 'A':
-                continue
-            sense_name = '%s_%s' % (pos[0].upper(), sense.lower())
-            if sense_name != 'N_tops':
-                sense_id = sense_names[sense_name]
-                lexicon[word][pos_ids[pos]].append(sense_id)
+        sense_key, synset_offset, sense_number, tag_cnt = line.split()
+        lemma, lex_sense = sense_key.split('%')
+        ss_type, lex_filenum, lex_id, head_word, head_id = lex_sense.split(':')
+        pos = pos_tags[int(ss_type)]
+        if pos is not None:
+            lexicon[lemma][pos].append(int(lex_filenum))
     return lexicon
 
 
@@ -105,7 +100,7 @@ def setup_vocab(src_dir, dst_dir):
         write_binary_vectors(str(vectors_src), str(dst_dir / 'vec.bin'))
     vocab = Vocab(data_dir=None, get_lex_props=get_lex_props)
     clusters = _read_clusters(src_dir / 'clusters.txt')
-    senses = _read_senses(src_dir / 'supersenses.txt')
+    senses = _read_senses(src_dir / 'wordnet' / 'index.sense')
     probs = _read_probs(src_dir / 'words.sgt.prob')
     for word in set(clusters).union(set(senses)):
         if word not in probs: