TabVec: Table Vectors for Classification of Web Tables

02/17/2018
by   Majid Ghasemi-Gol, et al.
0

There are hundreds of millions of tables in Web pages that contain useful information for many applications. Leveraging data within these tables is difficult because of the wide variety of structures, formats and data encoded in these tables. TabVec is an unsupervised method to embed tables into a vector space to support classification of tables into categories (entity, relational, matrix, list, and non-data) with minimal user intervention. TabVec deploys syntax and semantics of table cells, and embeds the structure of tables in a table vector space. This enables superior classification of tables even in the absence of domain annotations. Our evaluations in four real world domains show that TabVec improves classification accuracy by more than 20 state of the art systems, and that those systems require significant in domain training to achieve good results.

READ FULL TEXT
research
11/21/2019

Schemaless Queries over Document Tables with Dependencies

Unstructured enterprise data such as reports, manuals and guidelines oft...
research
02/16/2021

TableLab: An Interactive Table Extraction System with Adaptive Deep Learning

Table extraction from PDF and image documents is a ubiquitous task in th...
research
08/30/2018

Minimal inference from incomplete 2x2-tables

Estimates based on 2x2 tables of frequencies are widely used in statisti...
research
03/20/2019

On Extracting Data from HTML Tables

The Web provides many data in user-friendly tabular formats that are enc...
research
02/02/2023

Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles

Tabular data plays an essential role in many data analytics and machine ...
research
04/10/2023

Learning to Detect Touches on Cluttered Tables

We present a novel self-contained camera-projector tabletop system with ...

Please sign up or login with your details

Forgot password? Click here to reset