在使用深度學習(Deep Learning)方法於自然語言處理的問題時，我們通常會先將每一個詞以一個相對應的詞向量(Word Embedding)表示，再輸入至各式神經網路模型。當遭遇未登錄詞(Out-of-Vocabulary, OOV)的問題時，最常見的處理方式是略去該未登錄詞、以一個零向量表示或是用一個隨機產生的向量表示這個未登錄詞。就我們所知，在目前的研究裡，似乎仍未有一套合理且快速的做法，用於產生未登錄詞的詞向量表示法，並進一步地探索未登錄詞的詞向量對於任務成效的影響性。因此，本論文提出一套新穎的詞向量表示法學習技術，其目標是為未登錄詞產生一個較為合理且可靠的低維度向量表示法；除此之外，我們將進一步地把此一技術運用於中文機器閱讀理解任務之中，探究未登錄詞對於中文機器閱讀理解任務之影響，並驗證本論文所提出的詞向量表示法學習技術之成效。

When using Deep Learning methods in NLP-related tasks, we usually represent a word by using a low-dimensional dense vector, which is named the word embedding, and these word embeddings can then be treated as feature vectors for various neural network-based models. However, a major challenge facing such a mechanism is how to represent OOV words. There are two common strategies in practiced: one is to remove these words directly; the other is to represent OOV words by using zero or random vectors. To mitigate the flaw, we introduce an OOV embedding framework, which aims at generating reasonable low-dimensional dense vectors for OOV words. Furthermore, in order to evaluate the impact of the OOV representations, we plug the proposed framework into the Chinese machine reading comprehension task, and a series of experiments and comparisons demonstrate the good efficacy of the proposed framework.