Abstract
This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.
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