222 lines
8.4 KiB
Python
222 lines
8.4 KiB
Python
from sudachipy import tokenizer
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from sudachipy import dictionary
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try:
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from .transliteration_kana_to_hepburn import katakana_to_hepburn
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except ImportError:
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from transliteration_kana_to_hepburn import katakana_to_hepburn
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try:
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from .transliteration_context_rules import apply_context_rules
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except ImportError:
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from transliteration_context_rules import apply_context_rules
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class Transliterator:
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def __init__(self):
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self.tokenizer_obj = dictionary.Dictionary(dict_type="full").create()
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self.mode = tokenizer.Tokenizer.SplitMode.C
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@staticmethod
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def is_kanji(ch: str) -> bool:
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return '\u4e00' <= ch <= '\u9fff'
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@staticmethod
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def kata_to_hira(text: str) -> str:
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return "".join(
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chr(ord(c) - 0x60) if 'ァ' <= c <= 'ン' else c
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for c in text
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)
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@staticmethod
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def split_kanji_okurigana(surface: str, reading_kana: str, use_macron: bool = True):
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"""Split a single surface word and its kana reading into parts.
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Inputs:
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- surface: the surface form (may contain kanji + kana)
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- reading_kana: the katakana reading for the whole surface
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Output:
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- a list of dicts: [{"orig": str, "kana": str, "hira": str, "hepburn": str}, ...]
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Notes:
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- The function allocates portions of ``reading_kana`` to each contiguous
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kanji/non-kanji block in ``surface``. Allocation is heuristic: an
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initial allocation based on block length is used and any remainder is
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distributed left-to-right preferring kanji blocks.
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- This function is pure (no external side effects) and returns the
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constructed list.
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"""
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result = []
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# 表層を「漢字ブロック」と「非漢字ブロック」に分割
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buf = ""
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prev_is_kanji = None
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blocks = []
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for ch in surface:
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now_is_kanji = Transliterator.is_kanji(ch)
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if prev_is_kanji is None or now_is_kanji == prev_is_kanji:
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buf += ch
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else:
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blocks.append((prev_is_kanji, buf))
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buf = ch
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prev_is_kanji = now_is_kanji
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if buf:
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blocks.append((prev_is_kanji, buf))
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# 読みを分配
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kana_left = reading_kana
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# We'll allocate kana to each block by initial guess = len(part) (characters)
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# and distribute any remaining kana left-to-right preferring kanji blocks.
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kana_len = len(kana_left)
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# initial allocation per block
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allocs = [len(part) for _, part in blocks]
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allocated = sum(allocs)
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remaining = kana_len - allocated
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# distribute extra kana to kanji blocks first (left-to-right)
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if remaining > 0:
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for idx, (is_kan, _) in enumerate(blocks):
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if remaining <= 0:
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break
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if is_kan:
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allocs[idx] += 1
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remaining -= 1
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# if still remaining, distribute to all blocks left-to-right
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idx = 0
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while remaining > 0 and len(blocks) > 0:
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allocs[idx] += 1
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remaining -= 1
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idx = (idx + 1) % len(blocks)
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# if remaining < 0 (reading shorter than base), shrink allocations from right
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if remaining < 0:
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# remove from rightmost blocks as needed
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need = -remaining
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idx = len(blocks) - 1
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while need > 0 and idx >= 0:
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take = min(allocs[idx] - 1, need) if allocs[idx] > 1 else 0
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allocs[idx] -= take
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need -= take
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idx -= 1
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# now slice kana_left according to allocs
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pos = 0
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for (is_kan, part), cnt in zip(blocks, allocs):
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kana_for_part = kana_left[pos:pos+cnt]
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pos += cnt
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result.append({
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"orig": part,
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"kana": kana_for_part,
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"hira": Transliterator.kata_to_hira(kana_for_part),
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"hepburn": katakana_to_hepburn(kana_for_part, use_macron=use_macron)
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})
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return result
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def analyze(self, text: str, use_macron: bool = False):
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"""Tokenize ``text`` and produce per-subunit reading information.
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Returns a list of dicts for each token/sub-part with keys:
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- orig: original surface string (one or more characters)
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- kana: katakana reading for this part (may be adapted by context rules)
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- hira: hiragana reading (derived from kana)
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- hepburn: Latin transcription (derived from kana)
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Side-effects / notes:
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- The function calls ``apply_context_rules(results, use_macron=...)``
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which both mutates ``results`` in-place and returns it. This method
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safely accepts the returned list and then recalculates ``hira`` and
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``hepburn`` for entries whose ``kana`` was changed.
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- If rule application fails, analysis still returns the best-effort
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results.
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"""
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tokens = self.tokenizer_obj.tokenize(text, self.mode)
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results = []
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for t in tokens:
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surface = t.surface()
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reading = t.reading_form()
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pos = t.part_of_speech()
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if pos and pos[0] in ["記号", "補助記号", "空白"]:
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reading = surface
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if surface == reading:
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results.append({
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"orig": surface,
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"kana": reading,
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"hira": surface,
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"hepburn": surface,
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})
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continue
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# 単純に1文字ずつ処理
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if len(surface) == 1:
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# 1文字の場合はそのまま
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results.append({
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"orig": surface,
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"kana": reading,
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"hira": self.kata_to_hira(reading),
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"hepburn": katakana_to_hepburn(reading, use_macron=use_macron)
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})
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else:
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# 複数文字の場合は既存のユーティリティで分割
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parts = self.split_kanji_okurigana(surface, reading, use_macron=use_macron)
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results.extend(parts)
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# 文脈ルールを適用（別ファイル）
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try:
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results = apply_context_rules(results, use_macron=use_macron) or results
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except Exception:
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# ルール適用で失敗しても解析結果は返す
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pass
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# apply_context_rules が kana を書き換えた場合、hira と hepburn を再計算
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for entry in results:
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kana = entry.get("kana", "")
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if kana:
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entry["hira"] = self.kata_to_hira(kana)
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entry["hepburn"] = katakana_to_hepburn(kana, use_macron=use_macron)
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return results
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# --- テスト ---
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if __name__ == "__main__":
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import pprint
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test_cases = [
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"向こうへ行く",
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"行事を行う",
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"上がる",
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"上る",
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"入り込む",
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"何",
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"何が好き？",
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"何色が好き？",
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"何色ありますか？",
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"何語ですか？",
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"テーブルに色鉛筆は何色ありますか？"
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"美しい花を見る",
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"東京に行く",
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"漢字とカタカナの混在",
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"パーティーに行く",
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"コンピューターを使う",
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"シェアハウスに住む",
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"ヴァイオリンを弾く",
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"ギュウニュウを飲む",
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"ニューヨークに行く",
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"ラーメンを食べる",
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"チョコレートが好き",
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"SessionIDを取得する",
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"取り敢えず検索してみる",
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"見知らぬ土地で冒険する",
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"彼は優れたエンジニアです",
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" ".join(list("[]<>!@#$%^&*()_+-={}|\;:'\",.<>/?`~")),
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" ".join(list("「」＜＞！＠＃＄％＾＆＊（）＿＋－＝｛｝｜＼；：＇＂，．／？｀～")),
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" ".join(list("♪♫♬♭♮♯°℃℉№Å®©™✓✔✕✖★☆○●◎◇◆□■△▲▽▼※→←↑↓↔︎↕︎⇄⇅∞∴∵∷≪≫≦≧±×÷≠≈≡⊂⊃⊆⊇⊄⊅∪∩∈∋∅∀∃∠⊥⌒∂∇√∫∬∮∑∏∧∨¬⇒⇔∀∃∠⊥⌒∂∇√∫∬∮∑∏")),
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" ".join(list("😀😃😄😁😆😅😂🤣😊😇🙂"))
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]
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transliterator = Transliterator()
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for case in test_cases:
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pprint.pprint(transliterator.analyze(case), sort_dicts=False) |