Fix issue with parsing inconsistently formatted normalized words

README.md

@@ -14,10 +14,10 @@ dataset_info:
     sequence: string
   splits:
   - name: train
-    num_bytes: 2652674
+    num_bytes: 2653609
     num_examples: 2957
   download_size: 2871765
-  dataset_size: 2652674
+  dataset_size: 2653609
 task_categories:
 - token-classification
 language:

janes_tag.py

@@ -46,18 +46,30 @@ def word_info(wordlike_tag, _namespace):
 
     if wordlike_tag.tag in {f"{_namespace}w", f"{_namespace}pc"}:
         nes = None
-        if "lemma" in wordlike_tag.attrib:
-            words = [wordlike_tag.text.strip()]
-            lemmas = [wordlike_tag.attrib["lemma"].strip()]
-            msds = [wordlike_tag.attrib["ana"].strip()]
 
-        # If this happens, the word contains nested words indicating its normalized form
-        else:
+        children = list(iter(wordlike_tag))
+        if len(children) > 0:
+            # If this happens, the word contains nested words indicating its normalized form
             words, lemmas, msds = [], [], []
             for _child in wordlike_tag:
-                words.append(_child.attrib["norm"].strip())
-                lemmas.append(_child.attrib["lemma"].strip())
-                msds.append(_child.attrib["ana"].strip())
+                assert _child.tag in {f"{_namespace}w", f"{_namespace}pc"}, _child.tag
+
+                # Arbitrary words in the text have a normalized form that is formatted inconsistently and so it is
+                # unclear how to parse it correctly -> convention: always use information of the normalized words
+                if "norm" in _child.attrib:
+                    words.append(_child.attrib["norm"].strip())
+                    lemmas.append(_child.attrib["lemma"].strip())
+                    msds.append(_child.attrib["ana"].strip())
+                else:
+                    # These don't have linguistic annotations ¯\_(ツ)_/¯
+                    words.append(_child.text.strip())
+                    lemmas.append(_child.text.strip())
+                    msds.append("UNK")
+
+        else:
+            words = [wordlike_tag.text.strip()]
+            lemmas = [wordlike_tag.attrib["lemma"].strip()]
+            msds = [wordlike_tag.attrib["ana"].strip()]
 
         return words, lemmas, msds, nes