Start with improving the place results

tagger.py

@@ -1,33 +1,17 @@
 from nltk.tag.stanford import POSTagger
 from models import Article
+from analyze import *
 
 tagger = POSTagger('./stanford-postagger-full-2014-10-26/models/german-fast.tagger',
                    './stanford-postagger-full-2014-10-26/stanford-postagger-3.5.0.jar',
                     'UTF-8')
 
 for article in Article.select().limit(100):
-    pos = tagger.tag((article.place + " " + article.description).split())
-
-    # extract the places
-    places = []
-    is_matching = False
-    current_match = []
-    for tuple in pos:
-        if is_matching:
-            # when we're matching, the phrases we're looking for look like
-            # "Im S-Bahnhof Wedding"... the tags below mean
-            if tuple[1] in ("ART", "ADJA", "NN", "NE", "CARD"):
-                current_match.append(tuple)
-            else:
-                places.append(current_match)
-                current_match = []
-                is_matching = False
-        else:
-            # start matching when we have a preposition
-            if tuple[1] in ("APPR", "APPRART"):
-                is_matching = True
+    potential = get_potential_places(article.place, article.description)
+    places = improve_potential_places(potential)
 
     print(article.place)
     print(article.description)
     print()
-    print("Relevant: " + str(places))
+    print("Potential: " + str(potential))
+    print("Improved:  " + str(places))