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HiEve: A Corpus for Extracting Event Hierarchies from News Stories

机译:致命:从新闻报道中提取事件层次结构的语料库

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In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents. In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events. In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., superevent-subevent relations). We describe the process of manual annotation of HiEve. Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research. Preliminary experimental results are encouraging, with classifier performance reaching 58% F1-score, only 11% less than the inter-annotator agreement.
机译:在新闻报道中,事件提到表示不同空间和时间粒度的真实事件。新闻故事中的叙述通常描述一些粗糙的空间和时间粒度以及其子宫的一些现实世界事件。在这项工作中,我们展示了一个致力于认识到事件之间的时空遏制关系的语料库。在仇恨中,叙述是基于时尚储存关系的事件层次(即,超级子宫 - 子宫状关系)。我们描述了手动注释的过程。此外,我们建立一个监督分类器,用于识别事件之间的时空遏制,以作为未来研究的基准。初步实验结果是令人鼓舞的,分类器性能达到58%F1分数,仅比注册间协议小于11%。

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