Lai(2022)在訓練臺灣客家語料庫中「个」詞的多義詞詞意分辨模型時，參考了過去的「个」的研究(賴文英, 2012)來為此字的WSD模型訓練資料分類，但在實際的分類過程中，發現臺灣客語語料庫的詞類標記方式以及「个」的語法及語意複雜性導致標記人員難以順利執行原定的分類標準。本論文簡化了標記使用的資料分類標準，希望能使「个」詞的多義詞詞意分辨模型的訓練分類更為簡便，使機器學習更有效率。

When Lai (2022) trained the word sense disamguation model for morpheme GE in Taiwan Hakka Corpus, grammatical and functional classification of GE (賴文英, 2012) was adopted to construct its data annotation scheme. However, during the annotation process, the part of speech information of Taiwan Hakka Corpus and the syntactic and semantic complexity of GE let the annotaters find it difficult to abide by the original data annotation scheme. This research simplified the data annotation scheme, hoping to make the WSD data annotation process of GE easier, and therefore the WSD model training more efficient.

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