# Neural Ranking Models for Temporal Dependency Structure Parsing

arXiv: https://arxiv.org/abs/1809.00370 · HTML: https://mlsanity.com/paper/1809.00370/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2018.

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

- [Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering](https://mlsanity.com/paper/2310.19292/) (2023)
- [Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs](https://mlsanity.com/paper/2210.11787/) (2022)
- [TLEX: An Efficient Method for Extracting Exact Timelines from TimeML Temporal Graphs](https://mlsanity.com/paper/2406.05265/) (2024)

## Abstract (arXiv)

We design and build the first neural temporal dependency parser. It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a text into a temporal dependency tree structure. We evaluate our parser on two domains: news reports and narrative stories. In a parsing-only evaluation setup where gold time expressions and events are provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled parsing respectively, a result that is very competitive against alternative approaches. In an end-to-end evaluation setup where time expressions and events are automatically recognized, our parser beats two strong baselines on both data domains. Our experimental results and discussions shed light on the nature of temporal dependency structures in different domains and provide insights that we believe will be valuable to future research in this area.