Multidocument, Multilingual, and Multimodal Information Extraction for Real World Applications
November 2002
Mark T. Maybury, The MITRE Corporation
ABSTRACT
This keynote addresses current and future challenges in terminology and knowledge engineering focusing on multidocument, multilingual and multimodal information extraction. With some reports that humanity creates more than an exabyte (1018 bytes) of unique information each year, the imperative for tools to mitigate the size, heterogeneity, and complexity of knowledge collec-tions is a priority. After exemplifying this grand challenge in typical real world analytic envi-ronments, we briefly review the state of the art in information access. We note that automated systems exist that can return documents relevant to a particular subject with around 80% preci-sion but low recall. Automated document query incorporating relevance feedback has achieved near human performance. Extraction of named entities (Hirschman 1998) is over 90% accurate and extraction of relations among entities in specific domains is about 70-80% accurate. Also, documents can be summarized to about 20% of their source size without information loss, which can save users 50% of their original task time. Finally, prototype systems can respond to a sim-ple factual questions by returning answers from relevant documents with about 75% accuracy.
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