# Temporal Information Extraction by Predicting Relative Time-lines

arXiv: https://arxiv.org/abs/1808.09401 · HTML: https://mlsanity.com/paper/1808.09401/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [TIMEDIAL: Temporal Commonsense Reasoning in Dialog](https://mlsanity.com/paper/2106.04571/) (2021)
- [Probing Language Models for Understanding of Temporal Expressions](https://mlsanity.com/paper/2110.01113/) (2021)
- [AI Historian: Helping historians organize and verify person-centred temporal clues from dispersed historical narratives](https://mlsanity.com/paper/2608.29133/) (2026)
- [Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval](https://mlsanity.com/paper/2609.23880/) (2026)

## Abstract (arXiv)

The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results.