Start messing around with part of speech tagging

tagger.py

@@ -0,0 +1,33 @@
+from nltk.tag.stanford import POSTagger
+from models import Article
+
+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
+
+    print(article.place)
+    print(article.description)
+    print()
+    print("Relevant: " + str(places))