# Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture

arXiv: https://arxiv.org/abs/1703.05851 · HTML: https://mlsanity.com/paper/1703.05851/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2017.

## Built on by (most cited first, 11 of 11)

- [EventPlus: A Temporal Event Understanding Pipeline](https://mlsanity.com/paper/2101.04922/) (2021)
- [GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering](https://mlsanity.com/paper/2303.12320/) (2023)
- [Extracting Temporal Event Relation with Syntax-guided Graph Transformer](https://mlsanity.com/paper/2104.09570/) (2021)
- [Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction](https://mlsanity.com/paper/2210.04992/) (2022)
- [More than Classification: A Unified Framework for Event Temporal Relation Extraction](https://mlsanity.com/paper/2305.17607/) (2023)
- [Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?](https://mlsanity.com/paper/2410.10476/) (2024)
- [Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning](https://mlsanity.com/paper/2310.20236/) (2023)
- [Question Answering in Natural Language: the Special Case of Temporal Expressions](https://mlsanity.com/paper/2311.14087/) (2023)
- [TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems](https://mlsanity.com/paper/2406.14825/) (2024)
- [Only One Relation Possible? Modeling the Ambiguity in Event Temporal Relation Extraction](https://mlsanity.com/paper/2408.07353/) (2024)
- [Looking for the Bottleneck in Fine-grained Temporal Relation Classification](https://mlsanity.com/paper/2604.24620/) (2026)

## Abstract (arXiv)

In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is used to extract intra-sentence, cross-sentence, and document creation time relations. A "double-checking" technique reverses entity pairs in classification, boosting the recall of positive cases and reducing misclassifications between opposite classes. An efficient pruning algorithm resolves conflicts globally. Evaluated on QA-TempEval (SemEval2015 Task 5), our proposed technique outperforms state-of-the-art methods by a large margin.