Ilia Denisov
540ee32178
Build a committed Russian common-noun word list (dictprep/russian/scrabble.txt) from the RAN orthographic dictionary, for the Эрудит ruleset. - Stage 1 (Go, dictprep/ruwords): orfo_dict_2025.txt -> all.txt; extracts headwords, reconstructs "ед." singulars (suppressing plurals), pairs "и" variants. - Stage 2 (Python brain, dictprep/ru_stage2.py): OpenCorpora (mawo-pymorphy3) + libmorph + orthographic notes select common nouns (nom. sing.); --trace explains a word's fate, --dump writes the in-memory buckets. - libmorph C++ bridge (libmorph_check.cpp); manual_confirm.txt is merged in. - orfo_dict_2025.txt is the committed pdftotext source of truth. - See dictprep/README.md for methodology and reproducibility.
342 lines
13 KiB
Python
342 lines
13 KiB
Python
#!/usr/bin/env python3
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"""Stage 2 — the "brain" of the Russian Scrabble word-list pipeline.
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It reads the Stage-1 base word list (built once by ruwords so the heavy PDF is not
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re-parsed) together with the grammatical notes and the singular/variant structure, runs
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the whole noun-selection logic in memory, and writes a minimal result:
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dictprep/russian/scrabble.txt — the working dictionary (common nouns, nom. sing.)
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dictprep/russian/undefined.txt — the ambiguous tail, left for manual review
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(dictprep/russian/all.txt is the Stage-1 base.) Every other bucket — adjectives, verbs,
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the merged note-nouns, singulars, variants — stays in memory. Pass --dump to also write
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them; pass --trace WORD to ask how a single word did or did not reach the dictionary.
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Note: all.txt is a plain word list, so the grammatical notes, "ед." singulars and "и"
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variants are read from the pdftotext output (slov.txt) and the Stage-1 side files; the
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expensive PDF parse itself runs only once.
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Sources, most authoritative first: OpenCorpora (mawo-pymorphy3), libmorph (libmorph_check),
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and the orthographic dictionary's own notes. See dictprep/README.md.
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Run: ru-venv/bin/python dictprep/ru_stage2.py [--dump] [--trace WORD]
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"""
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import argparse
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import os
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import re
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import subprocess
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HERE = os.path.dirname(os.path.abspath(__file__))
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OUT_DIR = os.path.join(HERE, "russian")
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SLOV = os.path.join(OUT_DIR, "orfo_dict_2025.txt") # committed pdftotext output (source of truth)
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WL_FROM, WL_TO = 452, 168808 # 1-based inclusive bounds of the column word-list section
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OC_CACHE = "/tmp/oc_nouns.txt"
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LIBMORPH_BIN = os.path.join(HERE, "libmorph_check")
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ALPHABET = "абвгдеёжзийклмнопрстуфхцчшщъыьэюя"
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ORDER = {c: i for i, c in enumerate(ALPHABET)}
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PROPER = {"Name", "Surn", "Patr", "Geox", "Orgn", "Trad"}
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LIBMORPH_NOUN_CODES = set(range(7, 22)) | {24} # 7..21 plus 24 (pluralia tantum)
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ADJ_END = {"ая", "яя", "ое", "ее", "ье", "ья", "ьи"}
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VERB3 = ("ет", "ёт", "ит", "ют", "ут", "ает", "яет", "ует", "уют", "нет", "жет", "чет")
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GENPL = ("ов", "ёв", "ев", "ей")
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def key(w):
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return [ORDER.get(c, 99) for c in w]
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def destress(s):
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return "".join(c for c in s if ord(c) not in (0x0300, 0x0301)).lower()
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def cyr_ok(w):
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return 2 <= len(w) <= 15 and all(("а" <= c <= "я") or c == "ё" for c in w)
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def load(p):
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return [l.strip() for l in open(p, encoding="utf-8") if l.strip()] if os.path.exists(p) else []
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def write(path, words):
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os.makedirs(os.path.dirname(path), exist_ok=True)
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open(path, "w", encoding="utf-8").write("\n".join(sorted(set(words), key=key)) + "\n")
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import mawo_pymorphy3 # noqa: E402
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M = mawo_pymorphy3.MorphAnalyzer()
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D = M._dawg_dict
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def oc_noun_lemmas():
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"""Every common-noun lemma (nom. sing. / pluralia tantum) in OpenCorpora's words.dawg."""
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gp, pt = D.get_paradigm, D.parse_tag_string
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para0, tagc = {}, {}
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def g0(pid):
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r = para0.get(pid)
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if r is None:
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suf0, tag0, pre0 = gp(pid, 0)
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_, gr = pt(tag0)
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r = (pre0, suf0, gr)
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para0[pid] = r
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return r
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def gt(pid, idx):
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k = (pid, idx)
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r = tagc.get(k)
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if r is None:
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suf, tag, pre = gp(pid, idx)
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pos, gr = pt(tag)
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r = (suf, pre, pos, gr)
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tagc[k] = r
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return r
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out = set()
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for word, rec in D.words_dawg.iteritems():
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pid, idx = rec
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suf, pre, pos, gr = gt(pid, idx)
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if pos != "NOUN":
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continue
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pre0, suf0, gr0 = g0(pid)
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if (PROPER & gr) or (PROPER & gr0):
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continue
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stem = word[len(pre):len(word) - len(suf)] if suf else word[len(pre):]
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out.add(pre0 + stem + suf0)
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return {w for w in out if cyr_ok(w)}
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def oc_status(word):
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"""(is_common_noun, in_dictionary) for word, from OpenCorpora only."""
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parses = D.get_word_parses(word)
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if not parses:
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return False, False
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gp, pt = D.get_paradigm, D.parse_tag_string
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for pid, idx in parses:
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suf, tag, pre = gp(pid, idx)
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pos, gr = pt(tag)
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if pos == "NOUN":
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_, tag0, _ = gp(pid, 0)
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_, gr0 = pt(tag0)
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if not (PROPER & gr or PROPER & gr0):
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return True, True
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return False, True
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def libmorph_analyze(words):
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"""Map each word to (known, noun_lemma, codes) per libmorph; noun_lemma is None when it
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is not a common noun there. Empty result if the helper binary is not built."""
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words = list(words)
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if not words or not os.path.exists(LIBMORPH_BIN):
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return {}
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proc = subprocess.run([LIBMORPH_BIN], input="\n".join(words), capture_output=True, text=True)
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out = {}
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for w, line in zip(words, proc.stdout.split("\n")):
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fields = line.split("\t")
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known = fields[:1] == ["1"]
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codes, noun_lemmas = set(), []
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for field in fields[1:]:
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code, _, lex = field.partition(":")
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if code.isdigit():
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codes.add(int(code))
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if int(code) in LIBMORPH_NOUN_CODES:
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noun_lemmas.append(lex)
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lemma = (w if w in noun_lemmas else noun_lemmas[0]) if noun_lemmas else None
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out[w] = (known, lemma, codes)
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return out
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def build_notes():
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"""Map each headword (destressed, lowercased) to its grammatical note."""
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def is_hw(ch):
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o = ord(ch)
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return (0x0430 <= o <= 0x044F) or (0x0410 <= o <= 0x042F) or o in (0x0401, 0x0451, 0x0300, 0x0301)
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hmap = {}
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lines = open(SLOV, encoding="utf-8").read().split("\n")
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for l in lines[WL_FROM - 1:WL_TO]:
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s = l.lstrip()
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e = 0
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for ch in s:
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if is_hw(ch):
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e += 1
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else:
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break
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hw = destress(s[:e])
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if hw and hw not in hmap:
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hmap[hw] = destress(s[e:]).strip()
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return hmap
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def classify(w, note):
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"""Coarse part of speech of an out-of-dictionary word from its PDF note."""
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if note is None:
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return "amb"
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n = re.sub(r"\([^)]*\)", "", note).strip() # drop domain/etymology parentheticals
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if "кр. ф" in n or "кр.ф" in n or "прич." in n or "прил." in n:
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return "adj"
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ends = re.findall(r"-([а-яё]+)", n)
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if any(e in ADJ_END for e in ends):
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return "adj"
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if "сов." in n or "несов." in n or "безл." in n:
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return "verb"
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if w.endswith("ся"): # reflexive: no Russian noun ends in -ся
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return "verb"
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if any(e.endswith(VERB3) for e in ends) and not any(m in n for m in ("ед.", "тв.", "род.", "м.", "ж.", "с.")):
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return "verb"
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if n == "" and w.endswith(("ый", "ий", "ой", "ая", "ое", "ые", "ие", "яя", "ее")):
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return "adj"
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if "нескл" in n:
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return "noun" if any(g in n for g in ("м.", "ж.", "с.", "мн.")) else "amb"
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if ends:
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return "noun"
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if n == "" and w.endswith(("ать", "ять", "еть", "ить", "оть", "уть", "ыть", "ти", "чь")):
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return "verb"
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return "amb"
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def singular(w, note):
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"""Nominative singular of a noun headword from the PDF note (authoritative) or, for a
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plural headword without an explicit singular, the mawo lemma; pluralia tantum kept."""
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n = note or ""
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full = re.search(r"ед\.\s+([а-яё]+)", n)
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if full:
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return full.group(1)
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suf = re.search(r"ед\.\s+-([а-яё]+)", n)
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if suf:
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s = suf.group(1)
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i = w.rfind(s[0])
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return w[:i] + s if i > 0 else w
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ends = re.findall(r"-([а-яё]+)", re.sub(r"\([^)]*\)", "", n))
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if ends and ends[0].endswith(GENPL):
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for p in M.parse(w):
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if str(p.tag.POS) == "NOUN":
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return p.normal_form
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return w
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return w
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def build():
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"""Run the whole pipeline in memory. Returns the result sets plus a `fate` map giving
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every word's outcome, so a word's path can be traced or the buckets dumped."""
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oc = set(load(OC_CACHE)) or oc_noun_lemmas()
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if not os.path.exists(OC_CACHE):
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write(OC_CACHE, oc)
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hmap = build_notes()
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all_words = load(os.path.join(OUT_DIR, "all.txt"))
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ed_nouns = set(load("/tmp/ru_singulars.txt"))
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pairs = [tuple(p) for l in load("/tmp/ru_variants.txt") if len(p := l.split("\t")) == 2]
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pdf = [w for w in all_words if cyr_ok(w)]
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lm = libmorph_analyze(pdf)
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def to_singular(w):
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s = singular(w, hmap.get(w))
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return s if cyr_ok(s) else w
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fate = {}
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scrabble = set(oc)
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adj, verb, amb = [], [], []
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for w in pdf:
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oc_noun, oc_known = oc_status(w)
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if oc_noun:
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fate[w] = "scrabble: сущ. по OpenCorpora"
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continue
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lm_known, lm_lemma, _ = lm.get(w, (False, None, frozenset()))
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if lm_lemma is not None:
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s = lm_lemma if cyr_ok(lm_lemma) else to_singular(w)
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scrabble.add(s)
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fate[w] = "scrabble: сущ. по libmorph" + ("" if s == w else f" → {s}")
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continue
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if oc_known or lm_known:
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fate[w] = "отброшено: словарь знает как не-существительное"
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continue
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if w in ed_nouns:
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scrabble.add(w)
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fate[w] = "scrabble: ед.ч. по помете «ед.»"
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continue
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c = classify(w, hmap.get(w))
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if c == "noun":
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s = to_singular(w)
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scrabble.add(s)
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fate[w] = "scrabble: сущ. по помете орфословаря" + ("" if s == w else f" → {s}")
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elif c == "adj":
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adj.append(w)
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fate[w] = "отброшено: прилагательное (помета орфословаря)"
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elif c == "verb":
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verb.append(w)
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fate[w] = "отброшено: глагол (помета орфословаря)"
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else:
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amb.append(w)
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fate[w] = "undefined: неоднозначное (нет в словарях, помета не определяет)"
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# Manual confirmations: nouns the maintainer approved from the undefined tail.
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for w in load(os.path.join(OUT_DIR, "manual_confirm.txt")):
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if cyr_ok(w):
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scrabble.add(w)
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fate[w] = "scrabble: подтверждено вручную (manual_confirm.txt)"
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# Variant rescue: a word joined by "и" to a confirmed noun is itself a noun.
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pending = set(amb) - scrabble
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changed = True
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while changed:
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changed = False
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for a, b in pairs:
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for x, y in ((a, b), (b, a)):
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if x in scrabble and y in pending:
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scrabble.add(y)
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pending.discard(y)
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fate[y] = f"scrabble: вариант от «{x}» (через «и»)"
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changed = True
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undefined = [w for w in amb if w not in scrabble]
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return {
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"oc": oc, "scrabble": scrabble, "undefined": undefined,
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"adjectives": adj, "verbs": verb, "singulars": ed_nouns,
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"fate": fate, "all": set(all_words),
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}
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def trace(word, r):
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w = destress(word)
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if w in r["fate"]:
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return r["fate"][w]
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if w in r["scrabble"]:
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return "scrabble: лексикон OpenCorpora" if w in r["oc"] else "scrabble: производная/лемма"
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if w not in r["all"]:
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return "нет в russian_all (не извлечено на Stage 1 — нет в .pdf, либо имя собств./дефис/форма)"
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if not cyr_ok(w):
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return "отсеяно: длина или символы вне диапазона (2–15 кириллица)"
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return "не определено"
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def main():
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ap = argparse.ArgumentParser(description="Stage 2 brain: build the noun dictionary, trace a word, or dump buckets.")
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ap.add_argument("--dump", action="store_true", help="also write the in-memory buckets (adjectives, verbs, singulars, variants, fate)")
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ap.add_argument("--trace", metavar="WORD", help="report how WORD did or did not reach the dictionary, then exit")
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args = ap.parse_args()
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r = build()
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if args.trace:
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print(f"{args.trace}: {trace(args.trace, r)}")
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return
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write(os.path.join(OUT_DIR, "scrabble.txt"), r["scrabble"])
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print(f"=> dictprep/russian/scrabble.txt {len(r['scrabble'])}")
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print(f" undefined kept in memory: {len(set(r['undefined']))} (use --dump to write it)")
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if args.dump:
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write(os.path.join(OUT_DIR, "undefined.txt"), r["undefined"])
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write(os.path.join(OUT_DIR, "adjectives.txt"), r["adjectives"])
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write(os.path.join(OUT_DIR, "verbs.txt"), r["verbs"])
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write(os.path.join(OUT_DIR, "singulars.txt"), r["singulars"])
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fate_path = os.path.join(OUT_DIR, "fate.tsv")
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os.makedirs(OUT_DIR, exist_ok=True)
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with open(fate_path, "w", encoding="utf-8") as f:
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for w in sorted(r["fate"], key=key):
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f.write(f"{w}\t{r['fate'][w]}\n")
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print(f" dumped: undefined.txt ({len(set(r['undefined']))}), adjectives.txt, verbs.txt, singulars.txt, fate.tsv")
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if __name__ == "__main__":
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main()
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