# Content-Based Table Retrieval for Web Queries

## TL;DR

This paper introduces a ranking-based method combining features and neural networks for content-based table retrieval from web queries, supported by a large dataset and extensive experiments demonstrating its effectiveness.

## Contribution

It presents a novel ranking approach with designed features and neural models for table relevance, along with a large open-domain dataset for training and evaluation.

## Key findings

- The proposed method outperforms baseline models in relevance ranking.
- The dataset enables comprehensive evaluation of table retrieval methods.
- Challenges in semantic matching between queries and tables are identified.

## Abstract

Understanding the connections between unstructured text and semi-structured table is an important yet neglected problem in natural language processing. In this work, we focus on content-based table retrieval. Given a query, the task is to find the most relevant table from a collection of tables. Further progress towards improving this area requires powerful models of semantic matching and richer training and evaluation resources. To remedy this, we present a ranking based approach, and implement both carefully designed features and neural network architectures to measure the relevance between a query and the content of a table. Furthermore, we release an open-domain dataset that includes 21,113 web queries for 273,816 tables. We conduct comprehensive experiments on both real world and synthetic datasets. Results verify the effectiveness of our approach and present the challenges for this task.