Neural Ranking Models for Temporal Dependency Structure Parsing
Neural Ranking Models for Temporal Dependency Structure Parsing
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DOI：
10.18653/v1/d18-1371
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发表时间：
2018-09
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影响因子：
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通讯作者：
Yuchen Zhang;Nianwen Xue
中科院分区：
文献类型：
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作者：
Yuchen Zhang;Nianwen Xue
我们设计并构建了第一个神经时序依赖解析器。它利用具有最小特征工程的神经排序模型，并将文本中的时间表达式和事件解析为时间依赖树结构。我们评估我们的解析器在两个领域：新闻报道和叙事故事。在一个只分析的评估设置，其中提供黄金时间表达式和事件，我们的解析器达到0.81和0.70的F-分数无标记和标记的解析分别，结果是非常有竞争力的替代方法。在自动识别时间表达式和事件的端到端评估设置中，我们的解析器在两个数据域上都超过了两个强基线。我们的实验结果和讨论揭示了时间依赖结构在不同领域的性质，并提供了见解，我们相信这将是有价值的，在这一领域的未来研究。
We design and build the first neural temporal dependency parser. It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a text into a temporal dependency tree structure. We evaluate our parser on two domains: news reports and narrative stories. In a parsing-only evaluation setup where gold time expressions and events are provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled parsing respectively, a result that is very competitive against alternative approaches. In an end-to-end evaluation setup where time expressions and events are automatically recognized, our parser beats two strong baselines on both data domains. Our experimental results and discussions shed light on the nature of temporal dependency structures in different domains and provide insights that we believe will be valuable to future research in this area.