Anglicism Checker Methodology

Full disclosure of how we compute the clarity score: formula, weights, dictionary sources and tokenization decisions. No black boxes — every number is transparent and reproducible.

📐 Scoring formula

The final score is a number from 0 to 100. It's based on a smoothed density of weighted matches: each weight depends on severity, the necessity hierarchy, the domain, and context (for homonyms).

weight = SEV[level] × NECESSITY[tier] × domain_factor × context × strictness weighted = Σ weight context: exact=1.0, possible=0.3, native_sense=0 density = weighted / ( word_count + 60 ) ← smoothing for short texts penalty = 100 × density / ( density + 0.04 ) score = max( 0 , round( 100 − penalty ) )

Each match counts once; multi-word phrases count as one. Homonyms are judged by context: not penalized in their English sense, gently in ambiguous cases. Smoothing and a gradual curve keep the score stable across text lengths and comparable between texts.

⚖️ Weights and factors

A match's weight combines severity, the necessity hierarchy (VDS model), domain, and strictness. All values are baked into the code and identical across languages.

Severity level

Level	Weight	Meaning
low	0.5	Naturalised or neutral borrowing ("manager").
medium	1.0	OK in some contexts, redundant in others ("content").
high	2.0	Fully replaceable by a native word with no loss ("deadline").

Strictness mode

Mode	Factor	Meaning
lax	×0.6	Lax (×0.6). Ignores borrowings that are acceptable within the domain.
balanced	×1.0	Balanced (×1.0, default). Counts everything, but gently.
strict	×1.6	Strict (×1.6). Holds you to a higher bar.
purist	×2.4	Purist (×2.4). No leniency, even for naturalized words.

Necessity hierarchy (tier) — VDS Anglizismenindex model

Modifier	Factor	When applied
Needed (×0.15)	×0.15	No exact English equivalent — terminology. Barely penalized.
Nuance (×0.5)	×0.5	Adds a shade of meaning or has caught on. Penalized at half weight.
Avoidable (×1.0)	×1.0	A solid English replacement exists. Full weight.

Modifiers

Modifier	Factor	When applied
Domain match (×0.4)	×0.4	The word is acceptable in the chosen domain (e.g., an IT term in IT writing).
Homonym context	×1.0 / ×0.3 / 0	For homonyms: exact sense ×1.0, ambiguous ×0.3, native English sense — not counted.

🎯 Letter grades

Numeric score is mapped to a letter for quick orientation. Boundaries are hard-coded:

90–100

Excellent

75–89

Good

60–74

Mixed

40–59

Poor

0–39

Heavily borrowed

🧠 Tokenization & noise filtering

Before matching, we mask fragments that look like words but aren't. Masking preserves source offsets so highlighting stays accurate.

URLs (https://…) — masked entirely, so we don't flag "site" inside "https://site.com".
Email addresses — same.
@mentions and #hashtags — skipped as social markup.
Inline `code` in backticks — ignored.
camelCase and snake_case identifiers — ignored as code.
Multi-word phrases (e.g. "user experience") are matched before single words and consume their tokens, so they don't count twice.
Known brands and proper nouns (Apple, Yandex, ChatGPT) are skipped. The list extends per language via `meta.brands`.

📚 Per-language dictionaries

All dictionaries are freely available as JSON. Use them in your projects, cite in research, send corrections.

Frequently-cited entries carry their own source (`src`) — visible in the per-word popover. As curation progresses we are seeding every entry with a per-entry citation.

Russian 2805 · v2026-10-22-lic1 · CC BY-SA 4.0

Sources

Wiktionary contributors — English borrowings (CC BY-SA 4.0)
Timbrica editors

⬇ JSON

German 1187 · v2026-10-22-lic1 · CC BY-SA 4.0

Sources

Wiktionary contributors — English borrowings (CC BY-SA 4.0)
Timbrica editors

⬇ JSON

French 2460 · v2026-10-22-lic1 · CC BY-SA 4.0

Sources

Wiktionary contributors — English borrowings (CC BY-SA 4.0)
Timbrica editors

⬇ JSON

Spanish 968 · v2026-10-22-lic1 · CC BY-SA 4.0

Sources

Wiktionary contributors — English borrowings (CC BY-SA 4.0)
Timbrica editors

⬇ JSON

Portuguese 1030 · v2026-10-22-lic1 · CC BY-SA 4.0

Sources

Wiktionary contributors — English borrowings (CC BY-SA 4.0)
Timbrica editors

⬇ JSON

Korean 767 · v2026-10-22-lic1 · CC BY-SA 4.0

Sources

Wiktionary contributors — English borrowings (CC BY-SA 4.0)
Timbrica editors

⬇ JSON

Indonesian 745 · v2026-10-22-lic1 · CC BY-SA 4.0

Sources

Wiktionary contributors — English borrowings (CC BY-SA 4.0)
Timbrica editors

⬇ JSON

Arabic 663 · v2026-10-22-lic1 · CC BY-SA 4.0

Sources

Wiktionary contributors — English borrowings (CC BY-SA 4.0)
Timbrica editors

⬇ JSON

🤝 Honest about limitations

We aim to be a reference — so we're upfront about what the tool does and doesn't do.

What we don't do yet: Lemmatization is approximate (regex over root and endings). Proper nouns are not fully separated — "Site Smith" might be flagged. Contextual grammar is not considered. These improvements are on the roadmap; until they ship, the most reliable mode is Balanced strictness with naturalised enabled.

📜 License

The dictionaries are published under CC BY-SA 4.0. Part of the entries come from Wiktionary's lists of English borrowings, so reuse requires attribution — “Tembrica and Wiktionary contributors” — and a derived dataset must be shared under the same license.

CC BY-SA 4.0 · Wiktionary

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