本計畫鎖定目前口譯市場中需求較高且穩定的投資展望說明會英譯中源語語料，利用自動化詞彙抽取技術整理出領域相關詞彙與專有名詞。相同演講亦提供給口譯學生製備背景知識詞彙表，將之與數位工具所得結果加以比對，以評估此等電腦輔助翻譯工具是否有助於口譯學生更迅速擴展專業領域詞彙，以及對口譯教學的可能助益。結果發現口譯學生所製作之財經詞彙表與非監督式詞彙抽取技術所得結果仍有差距，因此在口譯實務上建議可採取（半）監督式電腦輔助詞彙抽取技術，而在口譯教學上則建議擴大專業領域文本數量，以提供教師備課重點參考。

This study aims to explore the possibility of using automatic term extraction technologies to assist interpreting trainees acquire domain-specific terminology more efficiently. A small monolingual corpus based on speeches collected from online investment seminars and relevant market analysis reports was first built and a few commonly used term extraction tools were applied to extract finance-and-investment-related terms. The same speeches were also made available to a select group of interpreting trainees, who were required to produce glossaries as part of their pre-class preparations. The term extraction results and the manually prepared glossaries were compared to determine how digital technologies may be best utilized to meet aspiring interpreters' needs. The comparison showed that there is still some gap between the kind of glossary deemed useful by the interpreter trainees and the list of automatically extracted terms. (Semi)-Supervised models may prove more helpful to produce the desired glossary. Interpreting educators may consider using such term extraction technologies in preparation for their classes that focus on specialized domains.