The TIPSTER Text Program incorporated new research efforts and fostered improvements to current developments. Under Phase III, the program continued to advance the state-of-the-art for the underlying technologies of Document Detection and Information Extraction. 数据挖掘研究院
TIPSTER research efforts focused on the following areas: 数据挖掘研究院
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Text summarization - an enhancement in information extraction and information retrieval to develop methods and algorithms to produce a summary for each document of interest or a single summary of multiple documents in a collection of interest.
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Merging search results - develop the means to merge results from different search engines while maintaining a relevance ranking for the retrieved information or to fuse the retrieved information with other items.
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Coreference resolutions - develop algorithms to resolve multiple text references to the same entity to better understand relationships among entities mentioned in a document.
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Customization methods - develop effective ways for a system administrator or end-user to port tools and techniques shown to work in one language or domain to other languages or domains. 数据挖掘实验室
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Multilingual capability enhancements. 数据挖掘研究院
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Improvements in natural language processing capabilities.
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Improvements in recall and precision for document detection (routing and retrieval) and information extraction algorithms.
