# Automatic Rule Generation for Time Expression Normalization

arXiv: https://arxiv.org/abs/2108.13658 · HTML: https://mlsanity.com/paper/2108.13658/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [CogCompTime: A Tool for Understanding Time in Natural Language Text](https://mlsanity.com/paper/1906.04940/) (2019)
- [TweeTime: A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter](https://mlsanity.com/paper/1608.02904/) (2016)

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

- [A Modular Approach for Multilingual Timex Detection and Normalization using Deep Learning and Grammar-based methods](https://mlsanity.com/paper/2304.14221/) (2023)
- [Conflict Detection for Temporal Knowledge Graphs:A Fast Constraint Mining Algorithm and New Benchmarks](https://mlsanity.com/paper/2312.11053/) (2023)

## Abstract (arXiv)

The understanding of time expressions includes two sub-tasks: recognition and normalization. In recent years, significant progress has been made in the recognition of time expressions while research on normalization has lagged behind. Existing SOTA normalization methods highly rely on rules or grammars designed by experts, which limits their performance on emerging corpora, such as social media texts. In this paper, we model time expression normalization as a sequence of operations to construct the normalized temporal value, and we present a novel method called ARTime, which can automatically generate normalization rules from training data without expert interventions. Specifically, ARTime automatically captures possible operation sequences from annotated data and generates normalization rules on time expressions with common surface forms. The experimental results show that ARTime can significantly surpass SOTA methods on the Tweets benchmark, and achieves competitive results with existing expert-engineered rule methods on the TempEval-3 benchmark.