# Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval

arXiv: https://arxiv.org/abs/1906.00041 · HTML: https://mlsanity.com/paper/1906.00041/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 30 of 37)

- [ColPali: Efficient Document Retrieval with Vision Language Models](https://mlsanity.com/paper/2407.01449/) (2024)
- [TABBIE: Pretrained Representations of Tabular Data](https://mlsanity.com/paper/2105.02584/) (2021)
- [Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks](https://mlsanity.com/paper/2201.09745/) (2022)
- [TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data](https://mlsanity.com/paper/2106.03096/) (2021)
- [PTab: Using the Pre-trained Language Model for Modeling Tabular Data](https://mlsanity.com/paper/2209.08060/) (2022)
- [TempTabQA: Temporal Question Answering for Semi-Structured Tables](https://mlsanity.com/paper/2311.08002/) (2023)
- [TCN: Table Convolutional Network for Web Table Interpretation](https://mlsanity.com/paper/2102.09460/) (2021)
- [Retrieving Complex Tables with Multi-Granular Graph Representation Learning](https://mlsanity.com/paper/2105.01736/) (2021)
- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)
- [Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution](https://mlsanity.com/paper/2408.10548/) (2024)
- [Visual Understanding of Complex Table Structures from Document Images](https://mlsanity.com/paper/2111.07129/) (2021)
- [Distilling Relation Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2110.15705/) (2021)
- [DeepJoin: Joinable Table Discovery with Pre-trained Language Models](https://mlsanity.com/paper/2212.07588/) (2022)
- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models](https://mlsanity.com/paper/2108.04049/) (2021)
- [Table Retrieval May Not Necessitate Table-specific Model Design](https://mlsanity.com/paper/2205.09843/) (2022)
- [Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective](https://mlsanity.com/paper/2105.08007/) (2021)
- [CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering](https://mlsanity.com/paper/2106.04441/) (2021)
- [Enhancing Temporal Understanding in LLMs for Semi-structured Tables](https://mlsanity.com/paper/2407.16030/) (2024)
- [Realistic Data Augmentation Framework for Enhancing Tabular Reasoning](https://mlsanity.com/paper/2210.12795/) (2022)
- [Metadata-based Data Exploration with Retrieval-Augmented Generation for Large Language Models](https://mlsanity.com/paper/2410.04231/) (2024)
- [Generating Table Vector Representations](https://mlsanity.com/paper/2110.15132/) (2021)
- [Enhancing Tabular Reasoning with Pattern Exploiting Training](https://mlsanity.com/paper/2210.12259/) (2022)
- [Leveraging Data Recasting to Enhance Tabular Reasoning](https://mlsanity.com/paper/2211.12641/) (2022)
- [Retrieval-Based Transformer for Table Augmentation](https://mlsanity.com/paper/2306.11843/) (2023)
- [InfoSync: Information Synchronization across Multilingual Semi-structured Tables](https://mlsanity.com/paper/2307.03313/) (2023)
- [ChartAB: A Benchmark for Chart Grounding & Dense Alignment](https://mlsanity.com/paper/2510.26781/) (2025)
- [DALL-M: Context-Aware Clinical Data Augmentation with LLMs](https://mlsanity.com/paper/2407.08227/) (2024)
- [Language Model Representations for Efficient Few-Shot Tabular Classification](https://mlsanity.com/paper/2602.15844/) (2026)
- [BERT Meets Relational DB: Contextual Representations of Relational Databases](https://mlsanity.com/paper/2104.14914/) (2021)

## Abstract (arXiv)

Tables contain valuable knowledge in a structured form. We employ neural language modeling approaches to embed tabular data into vector spaces. Specifically, we consider different table elements, such caption, column headings, and cells, for training word and entity embeddings. These embeddings are then utilized in three particular table-related tasks, row population, column population, and table retrieval, by incorporating them into existing retrieval models as additional semantic similarity signals. Evaluation results show that table embeddings can significantly improve upon the performance of state-of-the-art baselines.