提出了一种基于贝叶斯分类与机读词典的多义词排歧方法，通过小规模语料库的训练和歧义词在机读词典中的语义定义来完成歧义的消除。
A method based on the bayes and machine readable dictionary was proposed, which could disambiguate by the training of a small-scale corpus and the definition of semantic in machine dictionary.
本文设计、实现了一个唇部自动跟踪及检测系统，其在多媒体信号处理、语料库建立及唇读等系统中有重要应用。
The paper designs an automatic lip tracking and detecting system, which is important in multimedia signal processing, bimodal database and lip-reading system.
本文建立跨语言专利机读词典并在跨语言专利语料库的基础上研究语义消歧来解决跨语言专利中语言障碍问题。
A machine readable cross language dictionary was made to solve the language problems and at the mean time, ambiguity elimination was analyzed based on the cross language patent corpus.
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