# Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval

arXiv: https://arxiv.org/abs/2102.11127 · HTML: https://mlsanity.com/paper/2102.11127/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2021.

## Built on by (most cited first, 10 of 10)

- [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021)
- [Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2201.06885/) (2022)
- [Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes](https://mlsanity.com/paper/2210.12197/) (2022)
- [Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation](https://mlsanity.com/paper/2111.00678/) (2021)
- [Code Recommendation for Open Source Software Developers](https://mlsanity.com/paper/2210.08332/) (2022)
- [Relation-aware Heterogeneous Graph for User Profiling](https://mlsanity.com/paper/2110.07181/) (2021)
- [Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2210.05498/) (2022)
- [Learning To Rank Resources with GNN](https://mlsanity.com/paper/2304.07946/) (2023)
- [Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix](https://mlsanity.com/paper/2307.02126/) (2023)
- [Extracting Document Relations from Search Corpus by Marginalizing over User Queries](https://mlsanity.com/paper/2507.10726/) (2025)

## Abstract (arXiv)

The ad-hoc retrieval task is to rank related documents given a query and a document collection. A series of deep learning based approaches have been proposed to solve such problem and gained lots of attention. However, we argue that they are inherently based on local word sequences, ignoring the subtle long-distance document-level word relationships. To solve the problem, we explicitly model the document-level word relationship through the graph structure, capturing the subtle information via graph neural networks. In addition, due to the complexity and scale of the document collections, it is considerable to explore the different grain-sized hierarchical matching signals at a more general level. Therefore, we propose a Graph-based Hierarchical Relevance Matching model (GHRM) for ad-hoc retrieval, by which we can capture the subtle and general hierarchical matching signals simultaneously. We validate the effects of GHRM over two representative ad-hoc retrieval benchmarks, the comprehensive experiments and results demonstrate its superiority over state-of-the-art methods.