# Dating Documents using Graph Convolution Networks

arXiv: https://arxiv.org/abs/1902.00175 · HTML: https://mlsanity.com/paper/1902.00175/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 9 of 9)

- [Time Masking for Temporal Language Models](https://mlsanity.com/paper/2110.06366/) (2021)
- [S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers](https://mlsanity.com/paper/2203.06958/) (2022)
- [Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis](https://mlsanity.com/paper/2208.10970/) (2022)
- [It's High Time: A Survey of Temporal Question Answering](https://mlsanity.com/paper/2505.20243/) (2025)
- [A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech](https://mlsanity.com/paper/2106.03345/) (2021)
- [Improving RNN-T ASR Performance with Date-Time and Location Awareness](https://mlsanity.com/paper/2106.06183/) (2021)
- [Detecting Hostile Posts using Relational Graph Convolutional Network](https://mlsanity.com/paper/2101.03485/) (2021)
- [Timestamping Documents and Beliefs](https://mlsanity.com/paper/2106.14622/) (2021)
- [Temporal Sequencing of Documents](https://mlsanity.com/paper/2311.02578/) (2023)

## Abstract (arXiv)

Document date is essential for many important tasks, such as document retrieval, summarization, event detection, etc. While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web. Document Dating is a challenging problem which requires inference over the temporal structure of the document. Prior document dating systems have largely relied on handcrafted features while ignoring such document internal structures. In this paper, we propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach which jointly exploits syntactic and temporal graph structures of document in a principled way. To the best of our knowledge, this is the first application of deep learning for the problem of document dating. Through extensive experiments on real-world datasets, we find that NeuralDater significantly outperforms state-of-the-art baseline by 19% absolute (45% relative) accuracy points.