zh-TW and zh-CN previously had no `entity` section. Calling
`detect_entities(..., languages=("zh-TW",))` silently fell back to
English patterns (i18n/__init__.py:231-233), so no Chinese names
were ever extracted — Chinese-speaking users got zero people or
projects detected from their own notes.
This adds entity sections for both locales:
- `candidate_pattern`: common-surname-prefixed CJK n-grams (~100
surnames covering >95% of Taiwanese / PRC names), length capped
at {1,2} trailing chars so greedy matches don't swallow the
trailing verb character (e.g. 朱宜振說).
- `boundary_chars`: `\u4E00-\u9FFF` so the i18n loader's
script-aware wrap (introduced in #932) fires `\b` at CJK↔non-CJK
transitions. This is the same mechanism used for Devanagari,
applied to the CJK range.
- `person_verb_patterns`: Chinese verbs attach directly to the
name with no whitespace, so patterns are written as `{name}說`,
`{name}問`, `{name}決定` — no `\b` or `\s+` separators.
- `dialogue_patterns`: full-width colon `：`, Chinese quotes
「」『』, plus the standard Latin forms.
- `pronoun_patterns`: 他 / 她 / 它 / 他們 / 她們 / 您 / 咱.
- `stopwords`: ~140 common particles, pronouns, time expressions,
question words, conjunctions, UI nouns, and politeness forms.
**Known limitation** (explicitly covered by a test): CJK scripts
have no word delimiters, so a name flanked by CJK on both sides
with no punctuation or whitespace break is not extracted. This
is a fundamental limit of regex-based CJK entity detection —
resolving it would require a dictionary tokeniser. Realistic
Chinese technical writing contains enough non-CJK neighbours
(bullet lines, inline English, full-width punctuation, newlines)
that 3+ occurrences normally produce matches. Verified against a
realistic zh-TW PKM note: 朱宜振 extracted 11x from 8 sentences
with 0.99 person-classification confidence.
**Follow-ups** (separate PRs): same pattern for `ja` and `ko`,
both of which currently share the silent fallback-to-English bug.
Tests: 7 new tests in `tests/test_entity_detector.py`:
- `test_zh_tw_candidate_extraction_at_boundaries`
- `test_zh_tw_person_classification`
- `test_zh_tw_stopwords_filter_common_particles`
- `test_zh_tw_falls_back_to_english_for_non_cjk_names`
- `test_zh_cn_candidate_extraction`
- `test_zh_cn_and_zh_tw_union_covers_both_variants`
- `test_zh_tw_known_limitation_inline_name_no_boundary`
Full suite: 957 passed, 0 failed.
Lman Chu
683e940f70