论文标题
具有常识性约束的概率模型，用于基于模式的时间事实提取
A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction
论文作者
论文摘要
指定和/或生成用于从非结构化数据中提取事实信息的文本模式（例如，国家总统的总统）。基于模式的信息提取方法的效率和可传递性已被认可。但是，并非每种模式都是可靠的：一个主要的挑战是从不同的，有时甚至相互矛盾的提取中得出最完整，最准确的事实。在这项工作中，我们提出了一个概率图形模型，该模型在生成过程中提出了事实提取。它会自动不受任何监督而无需进行真实事实和模式可靠性。它具有两种新颖的设计，专门用于时间事实：（1）在两种类型的时间信号上模拟可靠性，包括文本和文本生成时间的时间标签； （2）IT将常识约束建模为可观察的变量。实验结果表明，我们的模型明显优于从新闻数据中提取真正的时间事实的现有方法。
Textual patterns (e.g., Country's president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.
