diff --git a/bin/parser/train.py b/bin/parser/train.py
index 3312a0cf3..7670ca81a 100755
--- a/bin/parser/train.py
+++ b/bin/parser/train.py
@@ -19,6 +19,7 @@ from spacy.en import English
 from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir
 
 from spacy.syntax.parser import GreedyParser
+from spacy.syntax.parser import OracleError
 from spacy.syntax.util import Config
 
 
@@ -36,7 +37,7 @@ def read_tokenized_gold(file_):
         labels = []
         tags = []
         for i, line in enumerate(sent_str.split('\n')):
-            word, pos_string, head_idx, label = _parse_line(line)
+            id_, word, pos_string, head_idx, label = _parse_line(line)
             words.append(word)
             if head_idx == -1:
                 head_idx = i
@@ -44,22 +45,29 @@ def read_tokenized_gold(file_):
             heads.append(head_idx)
             labels.append(label)
             tags.append(pos_string)
-        sents.append((ids_, words, heads, labels, tags))
+        text = ' '.join(words)
+        sents.append((text, [words], ids, words, tags, heads, labels))
     return sents
 
 
 def read_docparse_gold(file_):
     paragraphs = []
-    for sent_str in file_.read().strip().split('\n\n'):
+    for sent_str in file_.read().strip().split('<text>'):
+        if not sent_str.strip():
+            continue
         words = []
         heads = []
         labels = []
         tags = []
         ids = []
+        try:
+            raw_text, sent_str = sent_str.strip().split('</text>', 1)
+        except:
+            print sent_str
+            raise
         lines = sent_str.strip().split('\n')
-        raw_text = lines[0]
-        tok_text = lines[1]
-        for i, line in enumerate(lines[2:]):
+        tok_text = lines.pop(0)
+        for i, line in enumerate(lines):
             id_, word, pos_string, head_idx, label = _parse_line(line)
             if label == 'root':
                 label = 'ROOT'
@@ -180,7 +188,7 @@ def get_labels(sents):
 
 
 def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
-          gold_preproc=False):
+          gold_preproc=False, force_gold=False):
     dep_model_dir = path.join(model_dir, 'deps')
     pos_model_dir = path.join(model_dir, 'pos')
     if path.exists(dep_model_dir):
@@ -205,7 +213,10 @@ def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
         for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
                                                          gold_preproc=gold_preproc):
             nlp.tagger(tokens)
-            heads_corr += nlp.parser.train_sent(tokens, heads, labels, force_gold=False)
+            try:
+                heads_corr += nlp.parser.train_sent(tokens, heads, labels, force_gold=force_gold)
+            except OracleError:
+                continue
             pos_corr += nlp.tagger.train(tokens, tag_strs)
             n_tokens += len(tokens)
         acc = float(heads_corr) / n_tokens
@@ -221,10 +232,13 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
     global loss
     nlp = Language()
     n_corr = 0
+    pos_corr = 0
+    n_tokens = 0
     total = 0
     skipped = 0
     loss = 0
     with codecs.open(dev_loc, 'r', 'utf8') as file_:
+        #paragraphs = read_tokenized_gold(file_)
         paragraphs = read_docparse_gold(file_)
     for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
                                                      gold_preproc=gold_preproc):
@@ -232,6 +246,8 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
         nlp.tagger(tokens)
         nlp.parser(tokens)
         for i, token in enumerate(tokens):
+            pos_corr += token.tag_ == tag_strs[i]
+            n_tokens += 1
             if heads[i] is None:
                 skipped += 1
                 continue
@@ -240,14 +256,16 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
             n_corr += token.head.i == heads[i]
             total += 1
     print loss, skipped, (loss+skipped + total)
+    print pos_corr / n_tokens
     return float(n_corr) / (total + loss)
 
 
 def main(train_loc, dev_loc, model_dir):
     with codecs.open(train_loc, 'r', 'utf8') as file_:
-        train_sents  = read_docparse_gold(file_)
-    train(English, train_sents, model_dir, gold_preproc=False)
-    print evaluate(English, dev_loc, model_dir, gold_preproc=False)
+        #train_sents  = read_docparse_gold(file_)
+        train_sents  = read_tokenized_gold(file_)
+    #train(English, train_sents, model_dir, gold_preproc=True, force_gold=False)
+    print evaluate(English, dev_loc, model_dir, gold_preproc=True)
     
 
 if __name__ == '__main__':