# Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource

arXiv: https://arxiv.org/abs/1804.06020 · HTML: https://mlsanity.com/paper/1804.06020/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 17 of 17)

- [TIMEDIAL: Temporal Commonsense Reasoning in Dialog](https://mlsanity.com/paper/2106.04571/) (2021)
- [Zero-shot Temporal Relation Extraction with ChatGPT](https://mlsanity.com/paper/2304.05454/) (2023)
- [ROCK: Causal Inference Principles for Reasoning about Commonsense Causality](https://mlsanity.com/paper/2202.00436/) (2022)
- [Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts](https://mlsanity.com/paper/2205.01898/) (2022)
- [Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction](https://mlsanity.com/paper/2210.04992/) (2022)
- [Extracting Event Temporal Relations via Hyperbolic Geometry](https://mlsanity.com/paper/2109.05527/) (2021)
- [Event Temporal Relation Extraction with Bayesian Translational Model](https://mlsanity.com/paper/2302.04985/) (2023)
- [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://mlsanity.com/paper/2308.00002/) (2023)
- [ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19](https://mlsanity.com/paper/2105.01819/) (2021)
- [tieval: An Evaluation Framework for Temporal Information Extraction Systems](https://mlsanity.com/paper/2301.04643/) (2023)
- [Open-Domain Event Graph Induction for Mitigating Framing Bias](https://mlsanity.com/paper/2305.12835/) (2023)
- [Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs](https://mlsanity.com/paper/2403.15273/) (2024)
- [The Integer Linear Programming Inference Cookbook](https://mlsanity.com/paper/2307.00171/) (2023)
- [Causal schema induction for knowledge discovery](https://mlsanity.com/paper/2303.15381/) (2023)
- [Structured yet Bounded Temporal Understanding in Large Language Models](https://mlsanity.com/paper/2510.16685/) (2025)
- [Looking for the Bottleneck in Fine-grained Temporal Relation Classification](https://mlsanity.com/paper/2604.24620/) (2026)
- [Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction](https://mlsanity.com/paper/2609.06731/) (2026)

## Abstract (arXiv)

Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops such a resource -- a probabilistic knowledge base acquired in the news domain -- by extracting temporal relations between events from the New York Times (NYT) articles over a 20-year span (1987--2007). We show that existing temporal extraction systems can be improved via this resource. As a byproduct, we also show that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks. The proposed system and resource are both publicly available.