diff --git a/syntax_gen.py b/syntax_gen.py
new file mode 100644
index 0000000..c93e99c
--- /dev/null
+++ b/syntax_gen.py
@@ -0,0 +1,107 @@
+import codecs
+import os
+import pickle
+import random
+
+import spacy
+
+TEMPLATE_CORPUS = 'austencorpus'
+CONTENT_CORPUS = 'lovecraftcorpus'
+
+print('Loading spaCy model... ', end='')
+nlp = spacy.load('en_core_web_lg')
+print('Done')
+
+
+def load_text_files(dirname):
+    for (dirpath, dirnames, filenames) in os.walk(dirname):
+        for filename in filenames:
+            with codecs.open(os.path.join(dirpath, filename),
+                             encoding='utf-8') as f:
+                yield f.read()
+
+
+def load_syntax(dirname):
+    full_text = ''
+    for text in load_text_files(dirname):
+        full_text += text
+    return nlp(full_text)
+
+
+def load_object_to_file(filename):
+    with open(filename, 'rb') as f:
+        return pickle.load(f)
+
+
+def save_object_to_file(filename, object):
+    with open(filename, 'wb') as f:
+        pickle.dump(object, f)
+
+
+def build_content_dict(content_syntax):
+    content_dict = {}
+    for word in content_syntax:
+        if word.tag not in content_dict:
+            content_dict[word.tag] = {}
+        if word.dep not in content_dict[word.tag]:
+            content_dict[word.tag][word.dep] = set()
+        content_dict[word.tag][word.dep].add(word)
+    return content_dict
+
+
+def find_closest_content_word(template_word, content_dict):
+    closest = None
+    closest_score = 0.0
+
+    if template_word.tag in content_dict:
+        if template_word.dep in content_dict[template_word.tag]:
+            content_word_set = content_dict[template_word.tag][template_word.dep]
+        else:
+            random_dep = random.choice(list(content_dict[template_word.tag].keys()))
+            content_word_set = content_dict[template_word.tag][random_dep]
+    else:
+        return None
+
+    for content_word in content_word_set:
+        if closest is None or template_word.similarity(content_word) > closest_score:
+            closest = content_word
+            closest_score = template_word.similarity(content_word)
+
+    return closest
+
+
+if __name__ == '__main__':
+    if os.path.exists('template_syntax.bin'):
+        print('Loading parsed template corpus... ', end='')
+        template_syntax = spacy.tokens.Doc(spacy.vocab.Vocab())
+        template_syntax.from_disk('template_syntax.bin')
+        print('Done')
+    else:
+        print('Parsing template corpus... ', end='')
+        template_syntax = load_syntax(TEMPLATE_CORPUS)
+        template_syntax.to_disk('template_syntax.bin')
+        print('Done')
+
+    if os.path.exists('content_syntax.bin'):
+        print('Loading parsed content corpus... ', end='')
+        content_syntax = spacy.tokens.Doc(spacy.vocab.Vocab())
+        content_syntax.from_disk('content_syntax.bin')
+        print('Done')
+    else:
+        print('Parsing content corpus... ', end='')
+        content_syntax = load_syntax(CONTENT_CORPUS)
+        content_syntax.to_disk('content_syntax.bin')
+        print('Done')
+
+    print('Building content_dict... ', end='')
+    content_dict = build_content_dict(content_syntax)
+    save_object_to_file('content_dict.bin', content_dict)
+    print('Done')
+
+    for template_word in template_syntax[0:100]:
+        closest_word = find_closest_content_word(template_word, content_dict)
+        if closest_word:
+            print(closest_word.text_with_ws, end='')
+        else:
+            print('<NOMATCH> ', end='')
+    import ipdb; ipdb.set_trace()