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deterministic multilingual parsing lexicon for WhitesLove housing and hiring services","maintainers":[{"name":"whiteamorality","email":"kubai.rita2@gmail.com"}],"readme":"# @whiteslove/parsing-lexicon\n\nDeterministic multilingual parsing primitives shared by Whiteslove housing and hiring services.\n\nThe package is the canonical home for lexical data, normalization and reusable deterministic extraction. It is intentionally network-free and does **not** contain UI copy, persistence rules, source orchestration or consumer-specific business logic.\n\n## What it covers\n\n### Geography\n\n- Countries and multilingual aliases for the markets used by Whiteslove services.\n- Canonical city, region, district, microdistrict, locality and metro matching.\n- Uzbekistan and Kazakhstan location extensions.\n- Ukraine city/region catalog with current and historical names, major/regional city detail and metro/location extensions.\n- Tashkent districts, metro, colloquial areas, residential complexes and POIs.\n- Odesa metropolitan entities with separate administrative, suburb/locality, informal-area, development-area, POI and search-cluster semantics.\n- Normalization for apostrophe/dash variants and Cyrillic search folding across RU/UZ/KZ/UA input.\n\n### Housing\n\n- Deal/action and listing-kind taxonomy.\n- Property/building type, condition, layout, furniture and listing-status context.\n- Rooms, bedrooms, floors, total floors, building year and area extraction.\n- Deposit, prepayment, utilities and commission/payment context.\n- Seller/agency/owner confidence and no-commission semantics.\n- Tenant policies, documents, financing, availability and location relations.\n- Infrastructure/proximity extraction such as walking time to metro.\n\n### Hiring\n\n- Candidate vs vacancy intent and non-content/spam classification.\n- Section-aware vacancy/CV parsing.\n- Profession taxonomy and profession context without duplicating consumer technology-skill catalogs.\n- Salary range, currency and pay-period extraction.\n- Experience requirements and seniority.\n- Work mode, employment types, schedules and probation.\n- Languages, CEFR levels and requirement relation (`required`, `preferred`, `notRequired`, `candidateHas`).\n- Work authorization, visa sponsorship, relocation, contracts, benefits and hiring status context.\n\n## Architecture\n\nConsumers should resolve data in this order:\n\n```text\nstructured source fields\n  -> shared canonical lexicon\n  -> deterministic parser\n  -> AI fallback / enrichment\n```\n\nLexical aliases belong here. Consumers keep source adapters, persistence, ranking/filtering semantics and UI/API contracts.\n\nThe package must remain dependency-light and must never require a runtime lexicon HTTP service, Redis or a message broker.\n\nLocation entries expose their alias-matching regex through a lazy `re` getter, compiled on first match rather than at merge/import time. Merge helpers must copy entry fields without using object spread on a full entry (`{ ...entry }`), since spread invokes getters and would compile every alias regex eagerly during module load. See `AUDIT.md` for the history of this constraint.\n\n## Usage\n\n```js\nimport {\n  canonicalCity,\n  canonicalTashkentMetro,\n  normalizeForMatch,\n  parseHousingStructured,\n  parseHiringContext,\n  parseHiringSalary,\n} from '@whiteslove/parsing-lexicon';\n\ncanonicalCity('Қарағанды', 'KZ');\n// Karaganda\n\ncanonicalTashkentMetro('метро Максим Горький');\n// Buyuk Ipak Yoli\n\nnormalizeForMatch('Oʻzbekiston');\n// o zbekiston\n\nparseHousingStructured('3 xona, 5/9, 78 m², depozit 500$, metroga 5 minut piyoda');\n\nparseHiringSalary('Salary: $3,000–4,500 per month');\n\nparseHiringContext(\n  'Senior Frontend Engineer. English B2 required. Remote. Visa sponsorship available.',\n  { mode: 'vacancy', title: 'Senior Frontend Engineer' },\n);\n```\n\nSubpath exports are available for consumers that need narrower modules, including `./geo`, `./locations`, `./housing-context`, `./housing-structured`, `./hiring-context`, `./hiring-professions`, `./housing-money`, `./money` and `./currency`.\n\n### Money & currency\n\nCurrency and magnitude vocabulary (symbols, ISO codes, spelled-out names, and\nregional stand-ins like \"у.е.\") lives here once, in `money-lexicon.js`. A\nconsumer that needs to know whether some text mentions money at all — a fast\npre-filter before running the heavier parsers below, e.g. when scanning many\nHTML card candidates scraped from a page — should use `moneyMentionPattern()`\nrather than hand-copying a currency symbol list. A hand-copied list silently\ndrifts from this one; that exact bug (a missing \"₸\"/KZT symbol, and a missing\n\"у.е.\" token) is why this section exists.\n\n```js\nimport {\n  moneyCurrencyPattern,   // just the currency alternation, e.g. for building a custom matcher\n  moneyMentionPattern,    // currency OR a bare magnitude word (\"15 млн\") — boundary-guarded\n  moneyCurrencyFromText,  // -> 'KZT' | 'USD' | ... | null\n} from '@whiteslove/parsing-lexicon/currency';\nimport { parseHousingPrice } from '@whiteslove/parsing-lexicon/housing-money';\nimport { parseSalary } from '@whiteslove/parsing-lexicon/money';\n\nconst looksLikeMoney = new RegExp(moneyMentionPattern(), 'iu');\nlooksLikeMoney.test('300 000 ₸ в месяц'); // true\nlooksLikeMoney.test('2 до 3 месяцев');    // false — \"м\" inside \"месяцев\" does not count\n\nmoneyCurrencyFromText('300 000 ₸ в месяц'); // 'KZT'\n\nparseHousingPrice('12 500 000 сум', 'UZS');\n// -> { amount: 12500000, currency: 'UZS' }\n```\n\nDo not write a source-local regex for \"does this text contain a price\" or\n\"what currency is this\" — both already exist here and are kept in sync with\nthe full currency/magnitude vocabulary used by `parseHousingPrice`/\n`parseSalary`. If a currency form you need is missing, add it to\n`CURRENCY_TERMS` in `money-lexicon.js` (and `CURRENCY_SYMBOL_CANDIDATES` if\nit's a standalone symbol) rather than working around the gap in the consumer.\n\n## Data-quality rules\n\n- Add aliases to the existing canonical entity instead of creating source-specific regex tables.\n- Keep administrative entities distinct from colloquial/search concepts.\n- Preserve ambiguity guards where a bare term can mean several things.\n- Structured source fields take precedence over inferred values.\n- Add a regression for every production parsing bug.\n- Canonical dictionaries are immutable at runtime.\n- Duplicate canonical/alias entries and unsupported language-key drift are covered by invariants.\n\n## Lexicon statistics\n\nSnapshot for `c509d92` (2026-08-25). The audit traverses exported lexicon data from `src/`, excludes functions, regular expressions and obvious service/path strings, then normalizes strings with Unicode NFKC, trim, whitespace collapse and lowercase.\n\n| Metric | Count |\n| --- | ---: |\n| Alias/translation-like string occurrences across exports | 153,617 |\n| Unique alias/translation strings, exact | 17,681 |\n| **Unique alias/translation strings, normalized** | **17,397** |\n| All exported lexicon strings, normalized | 17,406 |\n| Normalization merge groups | 282 |\n\nThe occurrence count is intentionally much larger than the unique count because aggregate modules re-export the same canonical datasets. For package size and vocabulary breadth, the normalized unique count is the useful number.\n\n### Vocabulary by domain\n\n| Domain | Occurrences across exports | Unique exact | Unique normalized |\n| --- | ---: | ---: | ---: |\n| Geography / locations | 64,898 | 10,149 | **9,967** |\n| Hiring — professions & skills | 6,738 | 2,350 | **2,324** |\n| Hiring — other semantics | 3,592 | 2,589 | **2,558** |\n| Housing | 2,554 | 1,975 | **1,971** |\n| Hiring — languages | 552 | 495 | **478** |\n| Country / source aliases | 170 | 126 | **126** |\n\nDomain rows are not additive: the same canonical string can be reachable through multiple exported structures and categories.\n\nLanguage totals are not inferred from script alone because many valid aliases are intentionally shared or untagged (for example names that are identical in RU/UK or EN/UZ Latin). A language-by-language table should therefore be generated only from explicitly language-tagged data rather than guessed from Unicode characters.\n\n## Development\n\nRequires Node.js 20+.\n\n```bash\nnpm install\nnpm test\nnpm pack\n```\n\n`npm pack` produces the distributable `@whiteslove/parsing-lexicon` tarball from the files declared in `package.json`.\n","readmeFilename":"README.md"}