此在此之前：生物医学领域的因果关系优先
This before That: Causal Precedence in the Biomedical Domain
摘要：
生物化学相互作用之间的因果关系在生物医学领域至关重要，因为它改变了个体相互作用的集合，例如，结合和磷酸化，到因果机制需要通知有意义的搜索和推理。在这里，我们分析了生物医学领域的因果优先性，有别于开放域，时间优先性。首先，我们描述了一个新的，手工注释的文本语料库的因果关系优先在生物医学领域。其次，我们使用这个语料库调查电池的优先级模型，包括基于规则的，基于特征的，和潜在的表示模型。表现最好的个体模型获得了43分的微观F1，接近简单的时间优先任务的最佳表现者。基于规则的模型和潜在表示模型的性能都优于基于规则的模型，但它们的性能是互补的。我们采用了基于筛子的架构来利用这种缺乏重叠的情况，实现了46分的微观F1得分。
英文摘要：
Causal precedence between biochemical interactions is crucial in the biomedical domain, because it transforms collections of individual interactions, e.g., bindings and phosphorylations, into the causal mechanisms needed to inform meaningful search and inference. Here, we analyze causal precedence in the biomedical domain as distinct from open-domain, temporal precedence. First, we describe a novel, hand-annotated text corpus of causal precedence in the biomedical domain. Second, we use this corpus to investigate a battery of models of precedence, covering rule-based, feature-based, and latent representation models. The highest-performing individual model achieved a micro F1 of 43 points, approaching the best performers on the simpler temporal-only precedence tasks. Feature-based and latent representation models each outperform the rule-based models, but their performance is complementary to one another. We apply a sieve-based architecture to capitalize on this lack of overlap, achieving a micro F1 score of 46 points.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
