Ilia Denisov
e17e945b41
build / dawg (pull_request) Successful in 1m33s
OpenCorpora/libmorph hand a noun reading to Stage 2 above the РАН note, so substantivized-adjective false nouns a dictionary misreads (нёбный, акцизный, велярный, …) reached scrabble.txt with no veto path — the earlier abbreviation filter only carved out Abbr+Fixd. ru_stage2.py now subtracts sources/scrabble_ru/manual_reject.txt last, after every admission path (OC seed / libmorph / note / manual_confirm / variant), mirroring manual_confirm.txt. The list seeds 62 hand-reviewed words — 43 pure adjectives plus 19 marginal (slang/archaic/jargon) substantivizations; genuine substantivized nouns (больной, знакомый, учёный, участковый, …) stay. scrabble.txt -62 (83206->83144); erudit.txt re-folded -62. The DAWGs are gitignored and rebuild from these lists in CI.
226 lines
13 KiB
Markdown
226 lines
13 KiB
Markdown
# Russian word-list preparation (`tools`)
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Builds the Russian **noun** word list for the Scrabble/Эрудит solver out of the official
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Russian academic **orthographic dictionary**, cross-checked against two independent
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morphological dictionaries.
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The goal of the pipeline is a list of **common nouns in the nominative singular**
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(`sources/scrabble_ru/scrabble.txt`), plus an ambiguous tail for manual review.
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> This directory is self-contained tooling for *building* the word list. It is not part
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> of the solver library. The committed result lives in `sources/scrabble_ru/`.
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## Source
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`orfo_dict_2025.pdf` — *Русский орфографический словарь РАН* (≈ 200 000 entries), the
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authority for **spelling**. It encodes declension type in its grammatical notes but does
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**not** reliably mark part of speech.
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- Source: <https://ruslang.ru/sites/default/files/doc/normativnyje_slovari/orfograficheskij_slovar.pdf>
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- Mirror: <https://rus-gos.spbu.ru/index.php/dictionary>
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The PDF is git-ignored (large, third-party); place it here as `orfo_dict_2025.pdf`. Its
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pdftotext output is committed as `russian/orfo_dict_2025.txt`, so the word list rebuilds
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from the text alone — the binary PDF is needed only to regenerate that text.
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## Outputs (`sources/scrabble_ru/`)
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The committed result is **three** files; every other bucket stays in the Stage-2
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process's memory (dump it with `--dump`, query it with `--trace WORD`).
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| File | Committed | Meaning |
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|------|:--:|---------|
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| `orfo_dict_2025.txt` | ✓ | the pdftotext output — the parsed source of truth (the PDF binary is not needed to rebuild). |
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| `all.txt` | ✓ | Stage 1 base: every clean Cyrillic headword/variant; a plural headword with a singular is replaced by that singular. |
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| `manual_confirm.txt` | ✓ | hand-reviewed nouns from the undefined tail; the brain merges them into the result. |
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| `manual_reject.txt` | ✓ | hand-vetoed false nouns (substantivized adjectives a dictionary misreads as nouns, e.g. нёбный); subtracted from the result last. |
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| `scrabble.txt` | ✓ | **Stage 2 result**: common nouns, nominative singular (+ pluralia tantum), length 2–15 — the working dictionary. |
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| `undefined.txt` | — | the ambiguous tail; kept in memory, written only with `--dump`. |
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`--dump` also writes `adjectives.txt`, `verbs.txt`, `singulars.txt` and `fate.tsv` (every
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word with the reason it did or did not reach the dictionary); these are git-ignored debug
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artifacts. Stage 1 also writes `/tmp/ru_{skip,singulars,variants}.txt`, intermediate inputs
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the brain consumes.
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## Prerequisites
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```sh
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# 1. pdftotext (Poppler)
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sudo apt-get install -y poppler-utils
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# 2. Go toolchain (Stage 1) — already required by the parent module
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# 3. Python + the OpenCorpora analyser (Stage 2)
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sudo apt-get install -y python3-venv python3-pip
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python3 -m venv ru-venv
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ru-venv/bin/pip install mawo-pymorphy3 # bundles OpenCorpora 2025 (words.dawg)
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# 4. libmorph — the independent morphological dictionary (Stage 2 cross-check)
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sudo apt-get install -y morphrus morphrus-dev moonycode-dev morphapi-dev
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g++ -std=c++17 -O2 tools/libmorph_check.cpp -lmorphrus -lmoonycode -o tools/libmorph_check
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```
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If `tools/libmorph_check` is absent, Stage 2 still runs — it simply drops libmorph from
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the stack and reports `libmorph_helper=MISSING`.
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## How to run
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```sh
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# Stage 0 — PDF -> plain text (committed as the source of truth; run once)
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pdftotext tools/orfo_dict_2025.pdf sources/scrabble_ru/orfo_dict_2025.txt
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# Stage 1 — build the base word list (Go): sources/scrabble_ru/all.txt + /tmp/ru_*.txt
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go run ./tools/ruwords
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# Stage 2 — the brain (Python + mawo + libmorph): writes scrabble.txt
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ru-venv/bin/python tools/ru_stage2.py
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# audit how a single word did or did not reach the dictionary (detailed, per-signal report)
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ru-venv/bin/python tools/ru_stage2.py --trace ндс
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# also write the in-memory buckets (undefined, adjectives, verbs, singulars, fate.tsv)
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ru-venv/bin/python tools/ru_stage2.py --dump
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```
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`-from`/`-to` (defaulting to 452/168808) bound the column word-list section of
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`russian/orfo_dict_2025.txt` (line 452 = the first entry `а1, …`; line 168808 = the last,
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`я́щурный`). The preface above line 452 is prose and is skipped. Verify these bounds if the
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PDF is re-exported.
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## Algorithm
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### Stage 1 — `ruwords` (Go)
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Per dictionary line in `[from, to]` it collects, normalised (stress marks U+0300/U+0301
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stripped, lowercased, `ё` kept, hyphenated/capitalised/non-Cyrillic rejected):
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- the **headword** (leading token). Leading whitespace including the form-feed `\f`
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pdftotext puts at every page top is trimmed — otherwise the first headword of each page
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is lost;
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- the **singular of a plural headword** when the entry gives it after `ед.`, in full
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(`ящеры, …, ед. ящер`) or as a replacement suffix (`…, ед. -вец`, spliced where the
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suffix best overlaps the headword); the plural is then dropped (a plural that has a
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singular is never needed) and the singular is also recorded (`/tmp/ru_singulars.txt`);
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- **variant headwords** after `и` that carry their own grammatical note
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(`аблатив, -а и аблятив, -а`; `регги и реггей, нескл.`), excluding inflected forms.
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Everything else (every maximal Cyrillic token not selected above) goes to
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`/tmp/ru_skip.txt`, a safety net for a later morphology re-check.
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### Stage 2 — `ru_stage2.py` (Python)
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Each Stage-1 word (length 2–15) is routed by three sources, most authoritative first:
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1. **OpenCorpora** (`words.dawg`, read directly — *not* the predictor): a common-noun
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reading ⇒ keep the OpenCorpora lemma. The full OpenCorpora common-noun lexicon is also
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added (so nouns absent from the PDF are included), **minus indeclinable abbreviations**
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(`Abbr`+`Fixd`: ндс, кпд, чп, …) — OpenCorpora tags those as nouns but they are not
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Scrabble words. See *Abbreviations, proper nouns & function words* below for the full rule.
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2. **libmorph** (independent dictionary, via `libmorph_check`): a common-noun reading ⇒
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keep the libmorph lemma. The two dictionaries are treated as **complementary** — a noun
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reading in *either* is enough (their disagreements were reviewed and resolved this way,
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since each is incomplete in different places). A singular reconstructed from "ед." that
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neither dictionary knows is accepted as a noun (the orthographic note attests it).
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3. A word **both dictionaries miss** is classified by the orthographic **note**
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(`-ая, -ое` ⇒ adjective; `-ть`, `сов./несов.` ⇒ verb; single genitive `-а/-и` or
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`нескл., м./ж./с.` ⇒ noun). A note-noun goes straight to `scrabble.txt`; an adjective or
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verb is dropped; anything undecided goes to `undefined.txt`.
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4. **Variant rescue**: when the dictionary joins two spellings with "и" (`травмопункт и
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травмпункт`, `регги и реггей`) and one is already a confirmed noun, the other is moved
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from review/undefined into the result as well, propagated transitively through chains.
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The plural-form variants the dictionaries already resolve never reach this step.
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5. **Manual overrides**: `manual_confirm.txt` adds maintainer-approved nouns from the
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undefined tail (before variant rescue, so they can anchor it); `manual_reject.txt` is
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subtracted **last**, removing false nouns a dictionary misreads — substantivized
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adjectives with no nominal use (`нёбный`, `акцизный`, …) — overriding every path above.
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The nominative singular always comes from the dictionary that recognised the word, or from
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the orthographic `ед.` note — never from a predictor guess (libmorph and the predictor
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mis-lemmatise out-of-dictionary words, e.g. `витебчане → витебчан` instead of `витебчанин`).
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### The libmorph bridge — `libmorph_check.cpp`
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libmorph (A. Kovalenko, MIT) ships as `libmorphrus.so`. `libmorph_check` is a thin
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stdin→stdout filter: one UTF-8 word per line in, one line out:
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```
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<known>\t<pos>:<lemma>\t<pos>:<lemma>...
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```
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`<known>` is `CheckWord` (1 = in the dictionary). `<pos>` is `wdInfo & 0x3f`, the part of
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speech. The codes were reverse-engineered (the docs omit the table):
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| codes | part of speech |
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|------|----------------|
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| **7–21, 24** | **noun** (all genders / declensions / animacy; pluralia tantum is 24) |
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| 1–3 | verb · 25, 27 adjective · 28–32 pronoun · 33–36 numeral |
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| 38–39 | **proper noun** (excluded) · 48–58 comparative/adverb · 49–53 function words |
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The analyser instance is requested with the key `libmorph.api.v4:utf-8` so words are
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passed and lemmas returned in UTF-8.
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## Abbreviations, proper nouns & function words
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OpenCorpora lists indeclinable abbreviations as common nouns (`Abbr`+`Fixd`), so seeding the
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result with its whole noun lexicon leaked non-words like **ндс, ст, ср, кпд, чп** into
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`scrabble.txt`. They are filtered as follows (see `oc_noun_lemmas`, `oc_abbr_fixd_only`,
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`oc_function_word` and the Stage-2 loop in `ru_stage2.py`):
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- An `Abbr`+`Fixd` noun is **never** admitted on OpenCorpora's word alone; the orthographic
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dictionary decides via its own note (`classify`):
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- attested as a **lowercase** РАН headword whose note is a noun ⇒ **kept** — the
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lexicalised abbreviations that are real words: **сельпо, под, ска, роно, врио, тв, фио,
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суперэвм** (present in `all.txt`, `classify == noun`);
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- everything else ⇒ **dropped**: bare letter-abbreviations not in `all.txt` (ндс, кпд, чп,
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гибдд, днк, …) and lowercase РАН headwords whose note is *not* a noun (гор, мин, про —
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«приставка»; об, по, со — «предлог»; сто — числительное).
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- A word OpenCorpora also reads as a **function word** (`CONJ`/`PRCL`) is dropped even if the
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note looks noun-like — **зато** (союз), and any like it.
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- A word whose OpenCorpora common-noun reading is an **inflected form** (its lemma is a
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different word) is not a headword and stays out — **ан** (род. мн. от «ана»; the proper
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noun «Ан», самолёт Антонова, is capitalised and never reaches `all.txt`).
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The discriminator is **not** the `(сокр.)` mark — сельпо carries it just like ндс:
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| word | in `all.txt` (lowercase РАН headword) | `classify` (РАН note) | OC `CONJ`/`PRCL` | verdict |
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|------|:---:|:---:|:---:|---------|
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| сельпо, под, ска, роно, врио, тв, фио, суперэвм | yes | noun | no | **keep** |
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| ндс, кпд, чп, гибдд, днк, … | no | — | no | drop (seed) |
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| гор, мин, про, об, по, со, сто, прим | yes | not noun | — | drop (note) |
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| зато | yes | — | **yes** | drop (function word) |
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| ан | yes | noun | yes | drop (OC lemma ≠ word) |
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### Auditing a word and repeating this analysis
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`--trace WORD` prints a per-signal audit — Stage-1/`all.txt` membership, every OpenCorpora
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reading (POS, `Abbr`/`Fixd`, `CONJ`/`PRCL`), libmorph, the РАН note with its `classify`
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verdict, and the final outcome:
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```sh
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ru-venv/bin/python tools/ru_stage2.py --trace ндс # → НЕ в словаре: исключено из посева
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ru-venv/bin/python tools/ru_stage2.py --trace сельпо # → В СЛОВАРЕ: по помете орфословаря
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```
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To find a *whole class* of suspect words (the procedure used to catch the abbreviations above),
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read the signals straight from the dictionaries with the same primitives the pipeline uses:
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1. iterate `M._dawg_dict.words_dawg` and collect the lemmas with the grammemes you suspect
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(e.g. `Abbr`+`Fixd` on the lemma, no clean noun paradigm) — the candidate set;
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2. split it by `w in all.txt` (lowercase РАН headword), by `classify(w, note)` (РАН-note POS),
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and by an OpenCorpora `CONJ`/`PRCL` reading — these three signals separate real words from
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abbreviations / proper nouns / function words;
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3. review the candidate list by hand, add the distinguishing grammeme to the filter, re-run,
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and diff the rebuilt list against the previous one to confirm only the intended words moved
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(`--trace` each borderline decision).
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## Notes & caveats
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- The hard tail (≈ 35 000 Stage-1 words / our candidates) is in **no** morphological
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dictionary; only the orthographic dictionary attests them, so the PDF note is the sole
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signal there. Compound and very recent nouns (`робототехник`, `толкинист`) live here.
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- OpenCorpora and libmorph are near-equal in size (≈ 99 500 words each on `all.txt`)
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and ≈ 96 % overlapping, but **complementary** (each contributes ≈ 2 200 unique nouns),
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which is why both are kept. The mawo *predictor* "knows" ~98 % of everything by guessing
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and is therefore used only as a weak confirming vote, never as dictionary membership.
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- Licensing: OpenCorpora data is CC BY-SA 3.0; libmorph is MIT; the orthographic
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dictionary has its own copyright. A list derived from CC BY-SA data inherits that licence.
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