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arXiv · 2006.06436

A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction

Yang Zhou·Tong Zhao·Meng Jiang
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Abstract

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.

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Yang Zhou, Tong Zhao, Meng Jiang. 2020-06-11. A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction. https://arxiv.org/abs/2006.06436

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