5.1 研究總結
標籤分析中，本論文研究發現在我們設定的四種向量組合中，利用標籤(Tag) 來輔助分群，Tags as words 是最好組合的方式，優於其他三種組合方式。Words only 和 Tags only 特徵數都較少，無法達到好的效果，Words+tags 將標籤和摘 要的比例分開，沒有集中，效果比 Tags as words 差一點。在這部分實驗中可看 出 Constrained-PLSA 加入些許標記的資訊，很明顯的優於其他分群法，些許的 標記資訊，可以有效的提升效能。
在 Semi-supervised Leaning 實驗中，本論文所提出的方法，將背景知識 (Background Knowledge)，加入 Constrained-PLSA 演算法，能有效的提升效能 且 穩 定 的 效 果 。 在 實 驗 結 果 中 ， 在 兩 個 類 別 和 四 個 類 別 的 情 況 下 ， Constrained-PLSA 都能達到很好的效果，與其它方法做比較，大約都高 10%左右 的效能。Constrained-PLSA 只給予些許的標記(Labeled)資訊，使分群的效果達 到不錯的水準，讓系統能夠有效的應用於文件管理應用、資訊擷取等應用上。
5.2 未來研究
本論文之 Constrained-PLSA 在大部分的情況下都有不錯的效能，但在 Reuters 實驗中發現種子數 3%至 5%的情況下略輸 tSVM，但效果還是有一定的水 準，未來可以研究 Constrained-PLSA，要怎麼修改演算法，讓本論文之方法能 夠在這個部分也比 tSVM 效能好。
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