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

arXiv: https://arxiv.org/abs/2006.06436 · HTML: https://mlsanity.com/paper/2006.06436/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://mlsanity.com/paper/2308.00002/) (2023)
- [Implicit Temporal Reasoning for Evidence-Based Fact-Checking](https://mlsanity.com/paper/2302.12569/) (2023)

## Abstract (arXiv)

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.