WFGY/ProblemMap/GlobalFixMap/Language/stopword_and_morphology_controls.md

Stopword and Morphology Controls · Global Fix Map

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Lock language specific stopwords and morphology so retrieval remains stable across scripts and locales. Protect entities from stopword removal, version your lemmatizer, and verify with ΔS, λ, and coverage targets.

---

Open these first

• Visual map and recovery → rag-architecture-and-recovery.md
• End to end retrieval knobs → retrieval-playbook.md
• Traceability schema → retrieval-traceability.md
• Contract the payload → data-contracts.md
• Tokenizer variance → tokenizer_mismatch.md
• Mixed scripts → script_mixing.md
• Locale normalization → locale_drift.md
• Romanization rules → romanization_transliteration.md
• Proper nouns and aliases → proper_noun_aliases.md
• Language detection → query_language_detection.md
• Analyzer routing → query_routing_and_analyzers.md
• Bilingual eval sets → code_switching_eval.md

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Core acceptance targets

• ΔS(question, retrieved) ≤ 0.45 on three paraphrases and two seeds
• Coverage of target section ≥ 0.70
• λ convergent when switching between inflected forms and lemmas
• Removing stopwords never drops a required entity or negation token
• Rank@k does not regress more than 2 points after morphology changes

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What usually breaks

Symptom	Likely cause	Open this
Named entities vanish after preprocessing	stopword list removes particles that are part of names	proper_noun_aliases.md
Wrong meaning for negated statements	stopword filter removes “not” class tokens	retrieval-traceability.md
Duplicate docs across lemma and surface form	inconsistent stemming across index and query	tokenizer_mismatch.md
CJK recall collapses after adding stopwords	imported Latin stopword list applied to CJK	script_mixing.md
Turkish i and ı behave inconsistently	locale fold differs across stages	locale_drift.md
Romanized queries fail while native works	alias view absent and morphology applied only to native	romanization_transliteration.md

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Language family controls

Latin and Germanic

• Case fold with locale aware rules.
• Use light stemming or lemmatization, not both.
• Keep negation terms out of stopwords. Maintain a protected list.

Romance

• Normalize accents consistently.
• Prefer lemmatization for verbs and nouns.
• Maintain a protected list for names with articles or particles.

CJK

• Do not apply generic stopword lists.
• Use bigram or language specific tokenizers.
• Keep entities in dedicated fields without stopword removal.

Semitic RTL

• Normalize diacritics and width consistently.
• Use lemmatization that preserves roots only if evaluation passes ΔS and coverage.
• Keep a protected list for clitics that change meaning.

Indic

• Avoid generic lists from other languages.
• Use language specific analyzers and verify with bilingual eval.
• Protect named entities that share forms with common words.

Cyrillic and Greek

• Apply accent and width normalization.
• Prefer lemmatization over aggressive stemming.
• Maintain a protected entity list for inflected forms.

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Deterministic pipeline checklist

1. Version every component: stoplist_v, stemmer_v, lemma_v, normalize_v.
2. Define no stop zones for entity fields and citation fields.
3. Keep words filter with a protected list per language code.
4. Apply normalization before morphology, not after.
5. Use the same pipeline for indexing and querying.
6. Log a morphology fingerprint in traces and eval reports.

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Copy snippets

A. Protected term filter sketch

{
  "analysis": {
    "filter": {
      "keep_entities_en": {
        "type": "keep",
        "keep_words": ["New York", "AT&T", "Côte d'Ivoire", "Íñigo", "İstanbul"]
      }
    }
  }
}

B. Minimal morphology config record

{
  "language": "tr",
  "stoplist_v": "tr_core_1.2",
  "lemma_v": "tr_lemma_0.9",
  "normalize_v": "nfkc_fold_tr",
  "no_stop_fields": ["title_exact", "entity_exact"],
  "protected_list_hash": "sha256:..."
}

C. Trace fields to log

{
  "ΔS": 0.42,
  "λ_state": "<>",
  "coverage": 0.74,
  "language": "ar",
  "morph_fingerprint": "stop:ar_1.1|lemma:ar_0.8|norm:nfkc_1.0|keep:hash"
}

---

Eval protocol

• Use bilingual and code switching sets from code_switching_eval.md.
• For each query, test with and without stopword removal and with two morphology settings.
• Accept only if ΔS ≤ 0.45, coverage ≥ 0.70, λ convergent, and no loss of entity recall.
• Report Rank@k deltas for lemma vs surface forms.

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When to escalate

• ΔS stays ≥ 0.60 after morphology and stopword tuning → revisit analyzer routing and re chunking, see query_routing_and_analyzers.md and retrieval-playbook.md.
• Good top k but citations inconsistent → enforce schema and fix at the prompting layer, see retrieval-traceability.md and data-contracts.md.

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Copy paste prompt for the LLM step

You have TXTOS and the WFGY Problem Map loaded.

Task:
1) For {lang, script}, choose stopword and morphology settings that protect entities and negation.
2) Run cite then explain. If ΔS(question, retrieved) ≥ 0.60, propose the minimal structural fix.
3) Return JSON:
{ "stoplist_v": "...", "lemma_or_stem": "lemma|stem|none", "protected_terms": [...], "ΔS": 0.xx, "coverage": 0.xx, "λ_state": "→|←|<>|×" }
Keep it auditable and short.

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