* Messily use unsegmented sentences to train the parser

bin/parser/train.py

@@ -26,6 +26,7 @@ def read_tokenized_gold(file_):
     """Read a standard CoNLL/MALT-style format"""
     sents = []
     for sent_str in file_.read().strip().split('\n\n'):
+        ids = []
         words = []
         heads = []
         labels = []
@@ -35,10 +36,11 @@ def read_tokenized_gold(file_):
             words.append(word)
             if head_idx == -1:
                 head_idx = i
+            ids.append(id_)
             heads.append(head_idx)
             labels.append(label)
             tags.append(pos_string)
-        sents.append((words, heads, labels, tags))
+        sents.append((ids_, words, heads, labels, tags))
     return sents
 
 
@@ -49,31 +51,62 @@ def read_docparse_gold(file_):
         heads = []
         labels = []
         tags = []
+        ids = []
         lines = sent_str.strip().split('\n')
         raw_text = lines[0]
         tok_text = lines[1]
         for i, line in enumerate(lines[2:]):
-            word, pos_string, head_idx, label = _parse_line(line)
+            id_, word, pos_string, head_idx, label = _parse_line(line)
+            if label == 'root':
+                label = 'ROOT'
+            if pos_string == "``":
+                word = "``"
+            elif pos_string == "''":
+                word = "''"
             words.append(word)
-            if head_idx == -1:
-                head_idx = i
+            if head_idx < 0:
+                head_idx = id_
+            ids.append(id_)
             heads.append(head_idx)
             labels.append(label)
             tags.append(pos_string)
-        words = tok_text.replace('<SEP>', ' ').replace('<SENT>', ' ').split(' ')
+        heads = _map_indices_to_tokens(ids, heads)
+        words = tok_text.replace('<SENT>', ' ').replace('<SEP>', ' ').split()
+        #print words
+        #print heads
         sents.append((words, heads, labels, tags))
+        #sent_strings = tok_text.split('<SENT>')
+        #for sent in sent_strings:
+        #    sent_words = sent.replace('<SEP>', ' ').split(' ')
+        #    sent_heads = []
+        #    sent_labels = []
+        #    sent_tags = []
+        #    sent_ids = []
+        #    while len(sent_heads) < len(sent_words):
+        #        sent_heads.append(heads.pop(0))
+        #        sent_labels.append(labels.pop(0))
+        #        sent_tags.append(tags.pop(0))
+        #        sent_ids.append(ids.pop(0))
+        #    sent_heads = _map_indices_to_tokens(sent_ids, sent_heads)
+        #    sents.append((sent_words, sent_heads, sent_labels, sent_tags))
     return sents
 
+def _map_indices_to_tokens(ids, heads):
+    return [ids.index(head) for head in heads]
+    
+
+
 def _parse_line(line):
     pieces = line.split()
     if len(pieces) == 4:
-        return pieces[0], pieces[1], int(pieces[2]) - 1, pieces[3]
+        return 0, pieces[0], pieces[1], int(pieces[2]) - 1, pieces[3]
     else:
+        id_ = int(pieces[0])
         word = pieces[1]
         pos = pieces[3]
-        head_idx = int(pieces[6]) - 1
+        head_idx = int(pieces[6])
         label = pieces[7]
-        return word, pos, head_idx, label
+        return id_, word, pos, head_idx, label
 
 def get_labels(sents):
     left_labels = set()
@@ -113,7 +146,11 @@ def train(Language, sents, model_dir, n_iter=15, feat_set=u'basic', seed=0):
             tags = [nlp.tagger.tag_names.index(tag) for tag in tags]
             tokens = nlp.tokenizer.tokens_from_list(words)
             nlp.tagger(tokens)
-            heads_corr += nlp.parser.train_sent(tokens, heads, labels)
+            try:
+                heads_corr += nlp.parser.train_sent(tokens, heads, labels, force_gold=False)
+            except:
+                print heads
+                raise
             pos_corr += nlp.tagger.train(tokens, tags)
             n_tokens += len(tokens)
         acc = float(heads_corr) / n_tokens
@@ -122,7 +159,6 @@ def train(Language, sents, model_dir, n_iter=15, feat_set=u'basic', seed=0):
         random.shuffle(sents)
     nlp.parser.model.end_training()
     nlp.tagger.model.end_training()
-    #nlp.parser.model.dump(path.join(dep_model_dir, 'model'), freq_thresh=0)
     return acc
 
 
@@ -131,13 +167,13 @@ def evaluate(Language, dev_loc, model_dir):
     n_corr = 0
     total = 0
     with codecs.open(dev_loc, 'r', 'utf8') as file_:
-        sents = read_tokenized_gold(file_)
+        sents = read_docparse_gold(file_)
     for words, heads, labels, tags in sents:
         tokens = nlp.tokenizer.tokens_from_list(words)
         nlp.tagger(tokens)
         nlp.parser(tokens)
         for i, token in enumerate(tokens):
-            #print i, token.string, i + token.head, heads[i], labels[i]
+            #print i, token.orth_, token.head.orth_, tokens[heads[i]].orth_, labels[i], token.head.i == heads[i]
             if labels[i] == 'P' or labels[i] == 'punct':
                 continue
             n_corr += token.head.i == heads[i]
@@ -150,7 +186,8 @@ PROFILE = False
 
 def main(train_loc, dev_loc, model_dir):
     with codecs.open(train_loc, 'r', 'utf8') as file_:
-        train_sents  = read_tokenized_gold(file_)
+        train_sents  = read_docparse_gold(file_)
+    train_sents = train_sents
     if PROFILE:
         import cProfile
         import pstats