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

Abstract

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.

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19), 2019

References in corpus (7)

• Distributed Representations of Words and Phrases and their Compositionality
• Answering Table Queries on the Web using Column Keywords
• Ad Hoc Table Retrieval using Semantic Similarity
• EntiTables: Smart Assistance for Entity-Focused Tables
• On-the-fly Table Generation
• Design Patterns for Fusion-Based Object Retrieval
• TabVec: Table Vectors for Classification of Web Tables

Cited by in corpus (23)

• TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
• TURL: Table Understanding through Representation Learning
• Table Search Using a Deep Contextualized Language Model
• StruBERT: Structure-aware BERT for Table Search and Matching
• Retrieving Complex Tables with Multi-Granular Graph Representation Learning
• TCN: Table Convolutional Network for Web Table Interpretation
• Web Table Extraction, Retrieval and Augmentation: A Survey
• Distilling Relation Embeddings from Pre-trained Language Models
• Semantic Table Retrieval using Keyword and Table Queries
• Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective
• WTR: A Test Collection for Web Table Retrieval
• Table Structure Recognition using Top-Down and Bottom-Up Cues
• ARDA: Automatic Relational Data Augmentation for Machine Learning
• BERT Meets Relational DB: Contextual Representations of Relational Databases
• End-to-End Entity Detection with Proposer and Regressor
• GXJoin: Generalized Cell Transformations for Explainable Joinability
• CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering
• TABBIE: Pretrained Representations of Tabular Data
• Wiki-TabNER: Integrating Named Entity Recognition into Wikipedia Tables
• Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
• Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models
• Generating Table Vector Representations