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

现有的搜索引擎允许用户从互联网上的文本中检索有用的信息，但不能从广播新闻或其他录音等音频文件中检索有用的信息。用户必须浏览较长的录音才能找到相关的部分。尽管在处理自然语言的商业应用和研究原型方面取得了很大进展，但其信息检索系统的性能仍有待改进。我们提出了结合符号和统计方法来处理单词和句子意义的新方法。我们计划将它们应用于尚未尝试的几个任务中。我们特别建议将语义相似度方法应用于信息检索、自动语音识别和计算机辅助语言学习等任务中。我们专注于多语言和跨语言技术。

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.