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Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning

机译:加强学习查询扩展与信息提取的联合建模

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Information extraction about an event can be improved by incorporating external evidence. In this study, we propose a joint model for pseudo-relevance feedback based query expansion and information extraction with reinforcement learning. Our model generates an event-specific query to effectively retrieve documents relevant to the event. We demonstrate that our model is comparable or has better performance than the previous model in two publicly available datasets. Furthermore, we analyzed the influences of the retrieval effectiveness in our model on the extraction performance.
机译:通过纳入外部证据,可以提高关于事件的信息提取。在本研究中,我们提出了一种基于伪相关反馈的联合模型,并通过加固学习提供了基于伪相关反馈的查询扩展和信息提取。我们的模型生成了特定于事件查询,以有效地检索与事件相关的文档。我们展示了我们的模型是可比的,或者在两个公共可用数据集中的前一个模型具有更好的性能。此外,我们分析了在我们对提取性能模型中的检索效果的影响。

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