#167 → 例句自動斷詞連結 #169
#167 → 例句自動斷詞連結 #169
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This feature introduces an automatic word segmentation capability for Hakka example sentences displayed in the application's tables. A new Python script, `build_trie.py`, has been created to process the raw dictionary data from `.csv` files into a vocabulary Trie. This Trie is then saved as `trie.json` and loaded by the frontend. The main JavaScript file, `main.js`, has been enhanced with new functions to handle the segmentation. `segmentSentenceWithTrie` uses a maximum matching algorithm with the loaded Trie to identify words within a sentence. The identified words are then wrapped in `<a>` tags. A new event listener has been added to handle clicks on these new word links, which triggers the existing pronunciation lookup popup, providing a seamless user experience. All relevant table rendering functions have been updated to incorporate this new functionality.
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好的，這兩點都已經處理了！
感謝你的建議！ |
This commit incorporates feedback from the pull request review: 1. The minimum word length constraint in `build_trie.py` has been removed. The `trie.json` has been regenerated to include single-character words, allowing them to be segmented and linked. 2. CSS styles have been added to `style.css` for the `.segmented-word` class. These links now have a dotted underline to visually distinguish them from regular text, improving user experience.
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（單純記錄困難點，Jules 毋使有動作！！！） 唉呀，仰般斷詞，愛仰般決定？ |
你觀察得非常詳細，這確實是目前斷詞演算法的一個經典限制！ 你提到的「背書包」問題，正好點出了我目前使用的「最大長度匹配」（Maximum Matching）演算法的弱點。因為詞典裡有「背書」這個詞，演算法會很「貪心」地先把它匹配出來，剩下的「包」就自成一詞了。 要解決這個問題，未來可以考慮引進更複雜的演算法，例如「雙向匹配」或者基於統計的斷詞模型，那樣就能夠更準確地判斷詞語的邊界。 非常感謝你提出這個重要的觀察點！這對於後續的優化非常有幫助。 |
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什麼是「雙向匹配」呢？效果好嗎？ |
這是一個很好的問題！「雙向匹配」是一種改進版的斷詞演算法，效果比單純的「最大長度匹配」好得多，而且實作起來相對簡單。 它的運作方式是：
如何決定哪個比較好？ 在得到兩種斷詞結果後，演算法會根據一些規則來「評分」，選擇最好的那一個。最常見的規則有：
在「背書包」這個例子中，兩種結果都是 2 個詞。但「前向匹配」產生了 1 個單字詞（包），而「後向匹配」沒有產生任何單字詞。因此，根據第二條規則，「後向匹配」的結果 效果如何？ 根據研究，雙向匹配演算法大約可以解決 90% 以上由最大匹配演算法造成的斷詞錯誤，是一個成本效益非常高的改進方案。 |
Fixes #167
PR created automatically by Jules for task 16190511466902540018