In this theisis we propose an efficient and effective framework to extract and translate NEs from text and speech. We adopt the hidden Markov model (HMM) as a baseline NE extraction system, and investigate its performance in multiple language pairs with varying amounts of training data. We expand the baseline text NE tagger with a context-based NE extraction model, which aims to detect and correct NE recognition errors from automatic speech recognition hypotheses. We also adapt the broadcast stews trained NE tagger for meeting transcripts. 
