archive/phase_reports/audits/text-processing-strategy.md

NLP-3.5 — Text Processing Strategy

Problem

AraSpell processes text word-by-word with beam search decoding. Each word takes ~700ms. This means:

Text Size	Words	AraSpell Time	Feasible?
50 chars	7	4.8s	✅
100 chars	14	9.2s	✅
250 chars	35	26.0s	⚠️ Slow
500 chars	70	~49s (est.)	❌ Too slow
1000 chars	140	~98s (est.)	❌ Timeout risk
5000 chars	700	~490s (est.)	❌ Impossible

Solution: Adaptive Processing

Short Text (0–300 chars)

Full pipeline:

AraSpell → Grammar → Punctuation

All three models run. Maximum expected latency: ~40s.

Medium Text (300–1000 chars)

Skip AraSpell:

Grammar → Punctuation

Grammar and Punctuation handle the text. Expected latency: ~28s.

Large Text (1000+ chars)

Skip AraSpell:

Grammar → Punctuation

Same strategy as medium. Expected latency: ~30-34s.

Implementation

# In /api/analyze
text_len = len(current_text)
run_spelling = text_len <= 300
if not run_spelling:
    logger.info(f"Text length {text_len} > 300 — skipping AraSpell")

Rationale

• Grammar catches most errors that AraSpell would find in long texts
• Punctuation is independent of spelling
• Users get fast feedback on long texts instead of timeouts
• Short texts still get full spelling correction

Results

Text Size	Before	After
500 chars	>180s TIMEOUT	28.2s ✅
1000 chars	Would timeout	28.1s ✅
2000 chars	Would timeout	33.8s ✅