feat(nlp-service): keyword extraction (POS-filtered, deduped lemmas)

nlp-service/test_extractor.py

@@ -251,3 +251,50 @@ def test_date_after_english(nlp_en):
     date_from, date_to = extract_dates(doc, "en")
     assert date_from == "1900-01-01"
     assert date_to is None
+
+
+# ── Keyword extraction ───────────────────────────────────────────────────────
+
+def test_keywords_extracts_nouns(nlp_de):
+    from extractor import extract_keywords
+    # Use real NLP for POS tags; disable NER to avoid interference
+    doc = nlp_de("Briefe aus dem Krieg", disable=["ner"])
+    keywords = extract_keywords(doc, [])
+    # "Brief" (NOUN) and "Krieg" (NOUN) should appear as lemmas
+    assert "brief" in keywords
+    assert "krieg" in keywords
+
+
+def test_keywords_excludes_stopwords(nlp_de):
+    from extractor import extract_keywords
+    doc = nlp_de("Briefe aus dem Krieg", disable=["ner"])
+    keywords = extract_keywords(doc, [])
+    # "dem" is a stopword article — must not appear
+    assert "dem" not in keywords
+
+
+def test_keywords_excludes_per_ner_spans(nlp_de):
+    from extractor import extract_keywords
+    # Run full NLP for POS tags, then inject a PER span over "Hermann"
+    # "Briefe von Hermann": B=0..6, ' '=6, v=7..10, ' '=10, H=11..18
+    doc = nlp_de("Briefe von Hermann")
+    per_span = doc.char_span(11, 18, label="PER")
+    if per_span:
+        doc.ents = [per_span]
+        keywords = extract_keywords(doc, list(doc.ents))
+        assert "hermann" not in keywords
+
+
+def test_keywords_excludes_short_lemmas(nlp_de):
+    from extractor import extract_keywords
+    doc = nlp_de("Briefe an ihn", disable=["ner"])
+    keywords = extract_keywords(doc, [])
+    # "ihn" is 3 chars but is a stopword pronoun; "an" is 2 chars
+    assert "an" not in keywords
+
+
+def test_keywords_deduplicates(nlp_de):
+    from extractor import extract_keywords
+    doc = nlp_de("Brief Brief Krieg", disable=["ner"])
+    keywords = extract_keywords(doc, [])
+    assert keywords.count("brief") == 1