# Generating Table Vector Representations

arXiv: https://arxiv.org/abs/2110.15132 · HTML: https://mlsanity.com/paper/2110.15132/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://mlsanity.com/paper/2002.08910/) (2020)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval](https://mlsanity.com/paper/1906.00041/) (2019)
- [TCN: Table Convolutional Network for Web Table Interpretation](https://mlsanity.com/paper/2102.09460/) (2021)
- [Learning Semantic Annotations for Tabular Data](https://mlsanity.com/paper/1906.00781/) (2019)
- [Novel Entity Discovery from Web Tables](https://mlsanity.com/paper/2002.00206/) (2020)
- [Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)](https://mlsanity.com/paper/1907.00083/) (2019)
- [TabEAno: Table to Knowledge Graph Entity Annotation](https://mlsanity.com/paper/2010.01829/) (2020)

## Built on by (most cited first, 3 of 3)

- [CHORUS: Foundation Models for Unified Data Discovery and Exploration](https://mlsanity.com/paper/2306.09610/) (2023)
- [Named Entity Recognition in Industrial Tables using Tabular Language Models](https://mlsanity.com/paper/2209.14812/) (2022)
- [Adversarial Attacks on Tables with Entity Swap](https://mlsanity.com/paper/2309.08650/) (2023)

## Abstract (arXiv)

High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG). The focus of this paper is an evaluation of methods for table-to-class annotation, which is a sub-task of Table Interpretation (TI). We provide a formal definition for table classification as a machine learning task. We propose an experimental setup and we evaluate 5 fundamentally different approaches to find the best method for generating vector table representations. Our findings indicate that although transfer learning methods achieve high F1 score on the table classification task, dedicated table encoding models are a promising direction as they appear to capture richer semantics.