feat(normalizer): generate structured tags from Schlagwort + Inhalt fields

Adds tags.py module implementing a three-outcome heuristic:
- Individual-to-individual correspondence tags ("Clara an Herbert") → dropped
- Group/collective correspondence ("Clara an Kinder", "Walter an Geschwister") → Briefwechsel/<value>
- Semantic/event tags ("Brautbriefe", "Alltag", "zur Hochzeit") → Themen/<value>

Three correspondence patterns detected: space-an-space, starts-with-"an ",
and abbreviated-sender form ("Maria W.an Clara").

COLLECTIVE_TERMS in config.py extended with 17 plural/group relational terms
(söhne, brüder, schwiegereltern, cousinen, etc.) confirmed against the full Excel.

Also adds two-phase summary mining: every run emits review/tag-candidates.csv;
subsequent runs apply keywords from overrides/approved-themes.csv as Themen tags.

Outputs: canonical-documents.xlsx gets pipe-separated "Parent/Child" tag paths;
canonical-tag-tree.xlsx provides the full tag hierarchy for backend pre-import.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tools/import-normalizer/tests/test_documents.py

@@ -51,7 +51,7 @@ def test_to_canonical_resolves_and_flags():
     assert doc.sender_person_id == "de-gruyter-walter"
     assert doc.receiver_person_ids == ["de-gruyter-eugenie"]   # matched via maiden alias
     assert doc.date_iso == "1888-02-15" and doc.date_precision == "DAY"
-    assert doc.tags == ["Brautbriefe"]
+    assert doc.tags == ["Themen/Brautbriefe"]
     assert doc.needs_review == []
 
 def test_to_canonical_unmatched_and_unparsed():