Extracting Narrative Timelines as Temporal Dependency Structures
Extracting Narrative Timelines as Temporal Dependency Structures
复制标题
将叙事时间线提取为时间依赖结构
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Marie
中科院分区：
文献类型：
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作者：
O. Kolomiyets;Steven Bethard;Marie
我们提出了一种新的方法来刻画文本的时间线：时间依存结构，其中叙事中的所有事件通过偏序关系如之前、之后、重叠和同一性联系在一起。我们用时间依存关系树标注了儿童故事语料库，在事件词上达到了0.856的一致性，在事件之间的链接上达到了0.822的一致性，在排序关系标签上达到了0.700的一致性。我们比较了两种时态依赖结构的句法分析模型，结果表明，确定性非射影依赖句法分析器的性能优于基于图的最大生成树句法分析器，其标记连接准确率为0.647，标记树编辑距离为0.596。我们对依赖关系解析器错误的分析为未来的研究方向提供了一些见解。
We propose a new approach to characterizing the timeline of a text: temporal dependency structures, where all the events of a narrative are linked via partial ordering relations like BEFORE, AFTER, OVERLAP and IDENTITY. We annotate a corpus of children's stories with temporal dependency trees, achieving agreement (Krippendorff's Alpha) of 0.856 on the event words, 0.822 on the links between events, and of 0.700 on the ordering relation labels. We compare two parsing models for temporal dependency structures, and show that a deterministic non-projective dependency parser outperforms a graph-based maximum spanning tree parser, achieving labeled attachment accuracy of 0.647 and labeled tree edit distance of 0.596. Our analysis of the dependency parser errors gives some insights into future research directions.