# Temporal Event Knowledge Acquisition via Identifying Narratives

arXiv: https://arxiv.org/abs/1805.10956 · HTML: https://mlsanity.com/paper/1805.10956/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 6 of 6)

- [Where Do People Tell Stories Online? Story Detection Across Online Communities](https://mlsanity.com/paper/2311.09675/) (2023)
- [Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs](https://mlsanity.com/paper/2210.11787/) (2022)
- [How Toxic Can You Get? Search-based Toxicity Testing for Large Language Models](https://mlsanity.com/paper/2501.01741/) (2025)
- [Raply: A profanity-mitigated rap generator](https://mlsanity.com/paper/2407.06941/) (2024)
- [Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction](https://mlsanity.com/paper/2609.06731/) (2026)
- [Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives](https://mlsanity.com/paper/2610.17402/) (2026)

## Abstract (arXiv)

Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal "before/after" event knowledge across sentences in narrative stories. The double temporality states that a narrative story often describes a sequence of events following the chronological order and therefore, the temporal order of events matches with their textual order. We explored narratology principles and built a weakly supervised approach that identifies 287k narrative paragraphs from three large text corpora. We then extracted rich temporal event knowledge from these narrative paragraphs. Such event knowledge is shown useful to improve temporal relation classification and outperform several recent neural network models on the narrative cloze task.