# StruBERT: Structure-aware BERT for Table Search and Matching

arXiv: https://arxiv.org/abs/2203.14278 · HTML: https://mlsanity.com/paper/2203.14278/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2022.

## Built on by (most cited first, 24 of 24)

- [Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://mlsanity.com/paper/2305.13062/) (2023)
- [HYTREL: Hypergraph-enhanced Tabular Data Representation Learning](https://mlsanity.com/paper/2307.08623/) (2023)
- [SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation](https://mlsanity.com/paper/2210.12213/) (2022)
- [Towards Cross-Table Masked Pretraining for Web Data Mining](https://mlsanity.com/paper/2307.04308/) (2023)
- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [MGeo: Multi-Modal Geographic Pre-Training Method](https://mlsanity.com/paper/2301.04283/) (2023)
- [Time Series Language Model for Descriptive Caption Generation](https://mlsanity.com/paper/2501.01832/) (2025)
- [DAME: Domain Adaptation for Matching Entities](https://mlsanity.com/paper/2204.09244/) (2022)
- [Granite Embedding R2 Models](https://mlsanity.com/paper/2508.21085/) (2025)
- [Realistic Data Augmentation Framework for Enhancing Tabular Reasoning](https://mlsanity.com/paper/2210.12795/) (2022)
- [Scaling Experiments in Self-Supervised Cross-Table Representation Learning](https://mlsanity.com/paper/2309.17339/) (2023)
- [Multi-Intent Attribute-Aware Text Matching in Searching](https://mlsanity.com/paper/2402.07788/) (2024)
- [Enhancing Tabular Reasoning with Pattern Exploiting Training](https://mlsanity.com/paper/2210.12259/) (2022)
- [Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization](https://mlsanity.com/paper/2306.04787/) (2023)
- [InfoSync: Information Synchronization across Multilingual Semi-structured Tables](https://mlsanity.com/paper/2307.03313/) (2023)
- [2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language Models](https://mlsanity.com/paper/2409.19700/) (2024)
- [Enhancing Table Representations with LLM-powered Synthetic Data Generation](https://mlsanity.com/paper/2411.03356/) (2024)
- [Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval](https://mlsanity.com/paper/2309.10506/) (2023)
- [Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nesting](https://mlsanity.com/paper/2502.15819/) (2025)
- [TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunications](https://mlsanity.com/paper/2601.00691/) (2026)
- [Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference](https://mlsanity.com/paper/2502.11205/) (2025)
- [FollowTable: A Benchmark for Instruction-Following Table Retrieval](https://mlsanity.com/paper/2605.00400/) (2026)
- [PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries](https://mlsanity.com/paper/2605.18199/) (2026)
- [Polaris: Learning to Generate Table Descriptions from Retrieval Feedback](https://mlsanity.com/paper/2608.17171/) (2026)

## Abstract (arXiv)

A large amount of information is stored in data tables. Users can search for data tables using a keyword-based query. A table is composed primarily of data values that are organized in rows and columns providing implicit structural information. A table is usually accompanied by secondary information such as the caption, page title, etc., that form the textual information. Understanding the connection between the textual and structural information is an important yet neglected aspect in table retrieval as previous methods treat each source of information independently. In addition, users can search for data tables that are similar to an existing table, and this setting can be seen as a content-based table retrieval. In this paper, we propose StruBERT, a structure-aware BERT model that fuses the textual and structural information of a data table to produce context-aware representations for both textual and tabular content of a data table. StruBERT features are integrated in a new end-to-end neural ranking model to solve three table-related downstream tasks: keyword- and content-based table retrieval, and table similarity. We evaluate our approach using three datasets, and we demonstrate substantial improvements in terms of retrieval and classification metrics over state-of-the-art methods.