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Using Explicit Semantic Models to Track Situations across News Articles

机译:使用显式语义模型跟踪新闻文章的情况

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Online news is a rich information resource for learning about new, ongoing, and past events. Intelligence analysts, news junkies, and ordinary people all track developments in ongoing situations as they unfold over time and initiate queries to learn more about the past context of the events of interest to them. Brussell/STT (Situation Tracking Testbed) is an intelligent information system aimed at supporting this activity. Brussell employs a combination of explicit semantic models, information retrieval (IR), and information extraction (IE) in order to track a situation. It finds relevant news stories, organizes those stories around the aspects of the situation to which they pertain, and extracts certain basic facts about the situation for explicit representation. Brussell uses scripts as situation models for the episodes it tracks. Script instances are represented in CycL and stored in the Cyc knowledge-base. Models of ongoing situations can be reloaded and updated with new information as desired.
机译:在线新闻是一种丰富的信息资源,用于学习新的,正在进行的,持续的事件。情报分析师,新闻杂志和普通人在展开时期的情况下所有跟踪发展,因为它们随着时间的推移而展开,并开始查询,以了解更多关于他们感兴趣的事件的内容。布鲁塞尔/ STT(情况跟踪测试用)是旨在支持这项活动的智能信息系统。布鲁塞尔采用显式语义模型,信息检索(IR)和信息提取(即)以便跟踪情况。它找到了相关的新闻报道,围绕他们有关的情况的各个方面组织这些故事,并提取有关明确代表的情况的某些基本事实。布鲁塞尔使用脚本作为IT曲目的剧集的情况模型。脚本实例在CIRC中表示,并存储在CYC知识库中。可以根据需要重新加载和更新正在进行的情况的模型。

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