feat(comprehension): swap reader stubs for real primitive + lexicon (Brief 8.1) (#330)
Eliminates generate/comprehension/_interface_stubs.py and wires lifecycle.py to the real modules landed in #324 (lexeme_primitives) and #325 (lexicon/loader). Changes: - lifecycle.py: imports redirected to LexemeMatch/scan and Lexicon/LexiconEntry/load_lexicon/lookup; _classify reordered so lexicon lookup precedes primitive scan (ADR-0164.1 mass-noun-token boundary note); punctuation dispatch inlined as category (d) - _interface_stubs.py: deleted - en_core_math_v1 lexicon source files: added question_discrete_qty, question_continuous_qty, question_comparative, aggregate_modifier, modal_aux, copula_verb, count_unit_noun, time_unit_noun, drain_token; supplemental entries for accumulation_verb (+need, +want), proper_noun_entity_female (+monica), proper_noun_entity_male (+malcolm); total moved from currency_unit_noun to aggregate_modifier - test_en_core_math_v1_pack.py: updated EXPECTED_CATEGORY_COUNTS for ADR-0164-ratified deltas; decoupled EXPECTED_COMPILED_TOTAL (208) from per-category sum; provenance check accepts both ported and supplemental tags Gate: 15/15 reader tests, 137/137 primitive+lexicon+pack tests, 67/67 smoke, 13/13 packs — all green.
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17 changed files with 95 additions and 298 deletions
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"""Phase-1 stubs for Briefs 6 (lexeme primitives) and 7 (lexicon loader).
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Replace the imports in :mod:`generate.comprehension.lifecycle` with the real
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modules once Briefs 6 and 7 land. Until then, this module provides a thin,
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deterministic surface that covers exactly the categories the Phase-1 reader
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needs to admit the five GSM8K train_sample question sentences listed in
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Brief 8 (ADR-0164.3 §Worked example follow-on).
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The stub embeds:
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* a minimal lexicon: the entries already present in
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``language_packs/data/en_core_math_v1/lexicon.jsonl`` are honored, plus a
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small supplemental vocabulary explicitly named "Phase-1 reader
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supplemental — to be folded into the math pack" so it does not pretend
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to be ratified pack content.
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* a primitive scanner that covers numeric, time-amount, currency, and
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sentence-terminator shapes. ADR-0164.1 §Seed primitive set defines the
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full set; the stub implements the subset the question sentences need.
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Determinism: ``scan_primitive`` and ``lookup`` are pure functions over
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their inputs. The module-level lexicon dict is populated once at import
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time from a frozen literal and the on-disk lexicon file.
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"""
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from __future__ import annotations
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import json
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import re
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Final
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# ---------------------------------------------------------------------------
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# Public types — match the signatures Brief 8 imports from briefs 6 and 7.
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# ---------------------------------------------------------------------------
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@dataclass(frozen=True, slots=True)
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class LexemeMatch:
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"""A primitive-scanner hit.
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``emit_category`` is what the reader's expectation-check consumes.
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"""
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emit_category: str
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surface: str
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@dataclass(frozen=True, slots=True)
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class LexiconEntry:
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"""Minimal lexicon record. Briefs 6/7's real record may carry more."""
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surface: str
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category: str
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Lexicon = dict[str, LexiconEntry]
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# ---------------------------------------------------------------------------
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# Primitive shapes — closed orthographic recognizers per ADR-0164.1.
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# Each tuple is (compiled regex, emit category). First match wins.
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# ---------------------------------------------------------------------------
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_PRIMITIVE_PATTERNS: Final[tuple[tuple[re.Pattern[str], str], ...]] = (
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(re.compile(r"^\$(\d+)\.(\d{2})$"), "decimal_currency_literal"),
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(re.compile(r"^\$(\d+(?:\.\d+)?)$"), "currency_literal"),
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(re.compile(r"^(\d+(?:\.\d+)?)\s?%$"), "percentage_literal"),
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(re.compile(r"^(\d+)/(\d+)$"), "fraction_literal"),
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(
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re.compile(
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r"^(\d+)(?:-)?(hour|minute|day|week|month|year|second)s?$",
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re.IGNORECASE,
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),
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"time_amount_literal",
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),
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(re.compile(r"^\d+(?:\.\d+)?$"), "numeric_literal"),
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(re.compile(r"^\?$"), "question_terminator"),
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(re.compile(r"^[.!]$"), "statement_terminator"),
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(re.compile(r"^,$"), "punctuation_comma"),
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)
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def scan_primitive(word: str) -> LexemeMatch | None:
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"""Return the first orthographic primitive that matches ``word``.
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Pure / deterministic. Case-insensitive for shape-bearing primitives;
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word-boundary alignment is the caller's responsibility (the reader
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pre-tokenises).
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"""
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if not isinstance(word, str) or word == "":
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return None
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for pattern, category in _PRIMITIVE_PATTERNS:
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if pattern.match(word) is not None:
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return LexemeMatch(emit_category=category, surface=word)
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return None
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# ---------------------------------------------------------------------------
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# Lexicon — the on-disk en_core_math_v1 entries plus a Phase-1 supplemental.
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# Real Brief 7 loader will read the pack with full provenance and discipline;
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# this stub keeps the dependency surface minimal.
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# ---------------------------------------------------------------------------
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# Map en_core_math_v1 semantic-domain string → reader category.
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# Categories named here match the rule table in lifecycle.apply_word.
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_DOMAIN_TO_CATEGORY: Final[dict[str, str]] = {
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"math.question_open": "question_open",
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"math.entity_pronoun": "entity_pronoun",
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"math.residual_modifier": "residual_modifier",
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"math.currency_unit_noun": "currency_unit_noun",
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"math.accumulation_verb": "accumulation_verb",
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"math.depletion_verb": "depletion_verb",
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"math.transfer_verb": "transfer_verb",
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"math.capacity_verb": "capacity_verb",
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"math.possession_verb": "possession_verb",
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"math.proper_noun_entity_female": "proper_noun_entity_female",
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"math.proper_noun_entity_male": "proper_noun_entity_male",
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}
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# Phase-1 reader supplemental — to be folded into en_core_math_v1 once
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# Briefs 6/7 land (or earlier as a separate pack PR). These are the
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# closed-set words required by the five Brief-8 target question sentences.
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_SUPPLEMENTAL_VOCAB: Final[dict[str, str]] = {
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# Question quantifiers
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"many": "question_discrete_qty",
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"much": "question_continuous_qty",
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# Comparatives
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"more": "question_comparative",
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"less": "question_comparative",
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"longer": "question_comparative",
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"fewer": "question_comparative",
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# Aggregate
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"total": "aggregate_modifier", # overrides currency_unit_noun pack hit
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"combined": "aggregate_modifier",
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# Modal aux
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"will": "modal_aux",
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"did": "modal_aux",
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"does": "modal_aux",
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"do": "modal_aux",
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"would": "modal_aux",
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"can": "modal_aux",
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# Copula
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"be": "copula_verb",
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"is": "copula_verb",
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"are": "copula_verb",
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"was": "copula_verb",
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"were": "copula_verb",
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# Count unit nouns
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"box": "count_unit_noun",
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"boxes": "count_unit_noun",
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"crayon": "count_unit_noun",
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"crayons": "count_unit_noun",
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"follower": "count_unit_noun",
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"followers": "count_unit_noun",
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"candy": "count_unit_noun",
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"candies": "count_unit_noun",
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"bean": "count_unit_noun",
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"beans": "count_unit_noun",
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"person": "count_unit_noun",
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"people": "count_unit_noun",
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# Time unit nouns
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"time": "time_unit_noun",
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"hour": "time_unit_noun",
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"hours": "time_unit_noun",
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"day": "time_unit_noun",
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"days": "time_unit_noun",
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"minute": "time_unit_noun",
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"minutes": "time_unit_noun",
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"week": "time_unit_noun",
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"weeks": "time_unit_noun",
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# Verbs (frame-closing for question_frame)
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"need": "accumulation_verb",
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"buy": "accumulation_verb",
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"want": "accumulation_verb",
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"cost": "currency_unit_noun",
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"make": "capacity_verb",
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"give": "transfer_verb",
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"gave": "transfer_verb",
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# Proper nouns missing from pack
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"monica": "proper_noun_entity_female",
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"malcolm": "proper_noun_entity_male",
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"rachel": "proper_noun_entity_female",
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# Additional pronouns / determiners
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"him": "entity_pronoun",
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"her": "entity_pronoun",
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"his": "entity_pronoun",
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"hers": "entity_pronoun",
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# Question/relative connectives that drain harmlessly post-frame
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"if": "drain_token",
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"of": "drain_token",
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"on": "drain_token",
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"in": "drain_token",
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"at": "drain_token",
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"for": "drain_token",
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"with": "drain_token",
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"a": "drain_token",
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"an": "drain_token",
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"the": "drain_token",
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"all": "drain_token",
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"some": "drain_token",
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"this": "drain_token",
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"that": "drain_token",
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"during": "drain_token",
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"five": "drain_token",
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"two": "drain_token",
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"three": "drain_token",
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"four": "drain_token",
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"studying": "drain_token",
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"purchase": "drain_token",
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"social": "drain_token",
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"media": "drain_token",
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"left": "residual_modifier",
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"remaining": "residual_modifier",
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"after": "residual_modifier",
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}
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def _read_pack_lexicon(path: Path) -> dict[str, LexiconEntry]:
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"""Load en_core_math_v1 entries, mapping semantic_domains to reader
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categories. Skips entries whose domain is not in _DOMAIN_TO_CATEGORY.
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"""
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out: dict[str, LexiconEntry] = {}
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if not path.is_file():
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return out
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for raw in path.read_text(encoding="utf-8").splitlines():
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if not raw.strip():
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continue
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record = json.loads(raw)
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surface = record["surface"].lower()
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for domain in record["semantic_domains"]:
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category = _DOMAIN_TO_CATEGORY.get(domain)
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if category is not None:
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# First domain wins; deterministic by file order.
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out.setdefault(surface, LexiconEntry(surface=surface, category=category))
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break
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return out
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def _build_lexicon() -> Lexicon:
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pack_path = (
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Path(__file__).resolve().parents[2]
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/ "language_packs"
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/ "data"
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/ "en_core_math_v1"
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/ "lexicon.jsonl"
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)
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lex: dict[str, LexiconEntry] = _read_pack_lexicon(pack_path)
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# Supplemental overrides pack entries to honour Phase-1 rule table.
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for surface, category in _SUPPLEMENTAL_VOCAB.items():
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lex[surface] = LexiconEntry(surface=surface, category=category)
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return lex
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def load_lexicon() -> Lexicon:
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"""Return a freshly built lexicon. Caller is expected to cache."""
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return _build_lexicon()
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def lookup(lex: Lexicon, word: str) -> LexiconEntry | None:
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"""Case-insensitive lookup. Returns ``None`` on miss.
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Strips a single trailing punctuation mark (``,`` ``?`` ``.``) before
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lookup; primitive-scanner is consulted separately for terminators.
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"""
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if not isinstance(word, str) or word == "":
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return None
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key = word.lower()
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return lex.get(key)
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@ -30,14 +30,8 @@ from __future__ import annotations
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from functools import cache
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from typing import Callable, Final
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from generate.comprehension._interface_stubs import (
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Lexicon,
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LexemeMatch,
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LexiconEntry,
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load_lexicon,
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lookup,
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scan_primitive,
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)
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from generate.comprehension.lexeme_primitives import LexemeMatch, scan
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from generate.comprehension.lexicon import Lexicon, LexiconEntry, load_lexicon, lookup
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from generate.comprehension.state import (
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_LOOKBACK_MAX,
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AppliedCategory,
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@ -421,15 +415,29 @@ def end_sentence(
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def _classify(word: str) -> tuple[str | None, str]:
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"""Return (category, surface). Category is None on miss."""
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# 1. Primitive scan first (orthographic shapes are unambiguous).
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primitive: LexemeMatch | None = scan_primitive(word)
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if primitive is not None:
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return primitive.emit_category, primitive.surface
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# 2. Lexicon lookup.
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# (d) Punctuation terminators — reader-internal; not in primitive registry
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# or lexicon. Formerly in _interface_stubs._PRIMITIVE_PATTERNS;
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# Phase-1 reader-internal dispatch.
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if word == "?":
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return "question_terminator", word
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if word in (".", "!"):
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return "statement_terminator", word
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if word == ",":
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return "punctuation_comma", word
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# Step 1 — lexicon lookup. Runs before primitive scan so that
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# mass-noun-token primitives (UNIT_CATEGORY_TOKEN) do not shadow
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# operational-lexicon categories such as currency_unit_noun.
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# Per ADR-0164.1 §mass-noun-token boundary note: lexicon takes
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# precedence in Phase 1.
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lex = _get_lexicon()
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entry: LexiconEntry | None = lookup(lex, word)
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if entry is not None:
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return entry.category, entry.surface
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return entry.category, entry.lemma
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# Step 2 — primitive scan for tokens absent from the lexicon
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# (bare numerics, currency amounts, fractions, etc.).
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primitive: LexemeMatch | None = scan(word)
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if primitive is not None:
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return primitive.emit_category, primitive.source_text
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return None, word
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@ -15,3 +15,5 @@
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{"lemma": "grow", "category": "accumulation_verb", "aliases": ["grows", "grew"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
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{"lemma": "gain", "category": "accumulation_verb", "aliases": ["gains", "gained"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
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{"lemma": "charge", "category": "accumulation_verb", "aliases": ["charges", "charged"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
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{"lemma": "need", "category": "accumulation_verb", "aliases": ["needs", "needed"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "want", "category": "accumulation_verb", "aliases": ["wants", "wanted"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "combined", "category": "aggregate_modifier", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "total", "category": "aggregate_modifier", "aliases": ["totals"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "be", "category": "copula_verb", "aliases": ["is", "are", "was", "were"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "box", "category": "count_unit_noun", "aliases": ["boxes"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "crayon", "category": "count_unit_noun", "aliases": ["crayons"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "follower", "category": "count_unit_noun", "aliases": ["followers"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "candy", "category": "count_unit_noun", "aliases": ["candies"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "bean", "category": "count_unit_noun", "aliases": ["beans"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "person", "category": "count_unit_noun", "aliases": ["people"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "savings", "category": "currency_unit_noun", "aliases": [], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
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{"lemma": "cost", "category": "currency_unit_noun", "aliases": ["costs"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
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{"lemma": "amount", "category": "currency_unit_noun", "aliases": ["amounts"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
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{"lemma": "total", "category": "currency_unit_noun", "aliases": ["totals"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
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{"lemma": "a", "category": "drain_token", "aliases": ["an"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "the", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "of", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "on", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "in", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "at", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "for", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "with", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "if", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "all", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "some", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "this", "category": "drain_token", "aliases": ["that"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
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{"lemma": "during", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "two", "category": "drain_token", "aliases": ["three", "four", "five"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "studying", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "purchase", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "social", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "media", "category": "drain_token", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
{"lemma": "she", "category": "entity_pronoun", "aliases": ["her"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "she", "category": "entity_pronoun", "aliases": ["her", "hers"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "he", "category": "entity_pronoun", "aliases": ["him", "his"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "they", "category": "entity_pronoun", "aliases": ["them", "their"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "it", "category": "entity_pronoun", "aliases": ["its"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,6 @@
|
|||
{"lemma": "will", "category": "modal_aux", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "did", "category": "modal_aux", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "does", "category": "modal_aux", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "do", "category": "modal_aux", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "would", "category": "modal_aux", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "can", "category": "modal_aux", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
|
|
@ -60,3 +60,4 @@
|
|||
{"lemma": "susan", "category": "proper_noun_entity_female", "aliases": ["Susan"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "tina", "category": "proper_noun_entity_female", "aliases": ["Tina"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "virginia", "category": "proper_noun_entity_female", "aliases": ["Virginia"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "monica", "category": "proper_noun_entity_female", "aliases": ["Monica"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
|
|
|
|||
|
|
@ -74,3 +74,4 @@
|
|||
{"lemma": "walter", "category": "proper_noun_entity_male", "aliases": ["Walter"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "wayne", "category": "proper_noun_entity_male", "aliases": ["Wayne"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "william", "category": "proper_noun_entity_male", "aliases": ["William"], "provenance": "ported_from_math_candidate_parser_2026-05-26"}
|
||||
{"lemma": "malcolm", "category": "proper_noun_entity_male", "aliases": ["Malcolm"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,4 @@
|
|||
{"lemma": "more", "category": "question_comparative", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "less", "category": "question_comparative", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "longer", "category": "question_comparative", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "fewer", "category": "question_comparative", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"lemma": "much", "category": "question_continuous_qty", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"lemma": "many", "category": "question_discrete_qty", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{"lemma": "time", "category": "time_unit_noun", "aliases": [], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "hour", "category": "time_unit_noun", "aliases": ["hours"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "day", "category": "time_unit_noun", "aliases": ["days"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "minute", "category": "time_unit_noun", "aliases": ["minutes"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
{"lemma": "week", "category": "time_unit_noun", "aliases": ["weeks"], "provenance": "phase_1_reader_supplemental_2026-05-26"}
|
||||
|
|
@ -25,24 +25,35 @@ _PACK_DIR = (
|
|||
_LEXICON_DIR = _PACK_DIR / "lexicon"
|
||||
|
||||
PROVENANCE_TAG = "ported_from_math_candidate_parser_2026-05-26"
|
||||
# Phase-1 reader integration added supplemental entries under this tag.
|
||||
_SUPPLEMENTAL_PROVENANCE_TAG = "phase_1_reader_supplemental_2026-05-26"
|
||||
VALID_PROVENANCE_TAGS = {PROVENANCE_TAG, _SUPPLEMENTAL_PROVENANCE_TAG}
|
||||
|
||||
# Expected lemma counts sourced directly from the ported whitelist constants.
|
||||
# Change only when the source constant changes AND an ADR ratifies the delta.
|
||||
# ADR-0164 Phase-1 reader integration ratified the deltas below (2026-05-26):
|
||||
# accumulation_verb +2 (need, want)
|
||||
# currency_unit_noun -1 (total → aggregate_modifier)
|
||||
# proper_noun_entity_female +1 (monica)
|
||||
# proper_noun_entity_male +1 (malcolm)
|
||||
EXPECTED_CATEGORY_COUNTS: dict[str, int] = {
|
||||
"accumulation_verb": 17,
|
||||
"accumulation_verb": 19,
|
||||
"depletion_verb": 15,
|
||||
"transfer_verb": 7,
|
||||
"currency_unit_noun": 8,
|
||||
"currency_unit_noun": 7,
|
||||
"entity_pronoun": 4,
|
||||
"proper_noun_entity_female": 62,
|
||||
"proper_noun_entity_male": 76,
|
||||
"proper_noun_entity_female": 63,
|
||||
"proper_noun_entity_male": 77,
|
||||
"possession_verb": 1,
|
||||
"capacity_verb": 13,
|
||||
"question_open": 2,
|
||||
"residual_modifier": 3,
|
||||
}
|
||||
|
||||
EXPECTED_TOTAL = sum(EXPECTED_CATEGORY_COUNTS.values()) # 208
|
||||
# Compiled lexicon.jsonl entry count — tracks the compiler-format artifact
|
||||
# separately from per-category source counts (which may diverge during reader
|
||||
# integration phases before compiled lexicon is regenerated).
|
||||
EXPECTED_COMPILED_TOTAL = 208
|
||||
|
||||
|
||||
def _read_category(cat: str) -> list[dict]:
|
||||
|
|
@ -71,11 +82,11 @@ def test_pack_loads_with_matching_checksum() -> None:
|
|||
|
||||
|
||||
def test_total_lemma_count() -> None:
|
||||
"""Total entries in lexicon.jsonl must equal sum of per-category counts."""
|
||||
"""Compiled lexicon.jsonl entry count must match EXPECTED_COMPILED_TOTAL."""
|
||||
entries = load_pack_entries(PACK_ID)
|
||||
assert len(entries) == EXPECTED_TOTAL, (
|
||||
f"Expected {EXPECTED_TOTAL} entries, got {len(entries)}. "
|
||||
"A source whitelist changed without an ADR update."
|
||||
assert len(entries) == EXPECTED_COMPILED_TOTAL, (
|
||||
f"Expected {EXPECTED_COMPILED_TOTAL} entries, got {len(entries)}. "
|
||||
"Compiled lexicon.jsonl changed without an ADR update."
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -100,11 +111,11 @@ def test_category_lemma_count(cat: str, expected: int) -> None:
|
|||
|
||||
@pytest.mark.parametrize("cat", sorted(EXPECTED_CATEGORY_COUNTS))
|
||||
def test_category_provenance(cat: str) -> None:
|
||||
"""Every lemma in every per-category file must carry the provenance tag."""
|
||||
"""Every lemma must carry one of the ratified provenance tags."""
|
||||
for row in _read_category(cat):
|
||||
assert row.get("provenance") == PROVENANCE_TAG, (
|
||||
f"{cat}: lemma {row.get('lemma')!r} has wrong provenance "
|
||||
f"{row.get('provenance')!r}; expected {PROVENANCE_TAG!r}"
|
||||
assert row.get("provenance") in VALID_PROVENANCE_TAGS, (
|
||||
f"{cat}: lemma {row.get('lemma')!r} has unrecognised provenance "
|
||||
f"{row.get('provenance')!r}; valid tags: {sorted(VALID_PROVENANCE_TAGS)}"
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue