未來研究建議

雖然本研究對於運用 LSI 技術於自動化文件分類有初步的結果，也證明了其可行 性，但受限於時間與研究的規模，對於相關延伸問題無法再做深入的探討。因此針對本 研究採用的方法與前一節所提本研究的限制，對於本研究未來可持續進行的研究方向與 重點，提出下列幾點建議供後續研究參考。

一、 不僅探討單一分類的問題，還應探討多分類的問題。因為一份文件同時隸屬多個不 同的類別，比較符合現實世界的狀況。而 LSI 技術依其特性與目的，可能比較適 合處理多分類的問題。

二、 選用不同的文件資料集進行相關的研究與探討。本研究僅使用了從 Inspec on Disc 選出的部分文件資料，未來的研究可選取更多不同型態的資料，以廣泛及深入探討 運用 LSI 於自動化文件分類的可行性與效能。

三、 本研究在使用 k-NN 分類演算法時，並未對 k 值進行最佳化的處理。未來的研究 可對 k 值進行更有系統的探討。

四、 本研究利用傳統向量空間法與 LSI 法對照，分別搭配中心向量與 k-NN 兩種分類 演算法進行文件分類的實驗，未來的研究可考慮將 LSI 與其他不同的分類演算法 搭配，例如類神經網路、機率法等，以便於更充分瞭解運用 LSI 於自動化文件分 類的效能。

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