# TweeTime: A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter

arXiv: https://arxiv.org/abs/1608.02904 · HTML: https://mlsanity.com/paper/1608.02904/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2016.

## Built on by (most cited first, 3 of 3)

- [ScatterShot: Interactive In-context Example Curation for Text Transformation](https://mlsanity.com/paper/2302.07346/) (2023)
- [Automatic Rule Generation for Time Expression Normalization](https://mlsanity.com/paper/2108.13658/) (2021)
- [NTULM: Enriching Social Media Text Representations with Non-Textual Units](https://mlsanity.com/paper/2210.16586/) (2022)

## Abstract (arXiv)

We describe TweeTIME, a temporal tagger for recognizing and normalizing time expressions in Twitter. Most previous work in social media analysis has to rely on temporal resolvers that are designed for well-edited text, and therefore suffer from the reduced performance due to domain mismatch. We present a minimally supervised method that learns from large quantities of unlabeled data and requires no hand-engineered rules or hand-annotated training corpora. TweeTIME achieves 0.68 F1 score on the end-to-end task of resolving date expressions, outperforming a broad range of state-of-the-art systems.