Natural language processing for speech information retrieval and computer-assisted language learning

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Existing search engines allow a user to retrieve useful information from texts on the Internet, but not from audio documents such as broadcast news or other recordings. The user has to browse through long audio recordings to find relevant parts. Despite much progress both in commercial applications and in research prototypes that deal with natural language, their performance of information retrieval systems still needs to be improved. We propose new ways of combining symbolic and statistical methods for dealing with the meaning of words and sentences. We plan to apply them in several tasks where they have not yet been tried. In particular, we propose to apply semantic similarity methods to tasks in Information Retrieval, Automatic Speech Recognition, and Computer Assisted Language Learning. We focus on multiple languages and on cross-language techniques.