research-article

Context Retrieval for Web Tables

Authors:

Brian D. Ziebart,

Warren ShenAuthors Info & Claims

ICTIR '15: Proceedings of the 2015 International Conference on The Theory of Information Retrieval

Pages 251 - 260

https://doi.org/10.1145/2808194.2809453

Published: 27 September 2015 Publication History

Abstract

Many modern knowledge bases are built by extracting information from millions of web pages. Though existing extraction methods primarily focus on web pages' main text, a huge amount of information is embedded within other web structures, such as web tables. Previous studies have shown that linking web page tables and textual context is beneficial for extracting more information from web pages. However, using the text surrounding each table without carefully assessing its relevance introduces noise in the extracted information, degrading its accuracy. To the best of our knowledge, we provide the first systematic study of the problem of table-related context retrieval: given a table and the sentences within the same web page, determine for each sentence whether it is relevant to the table. We define the concept of relevance and introduce a Table-Related Context Retrieval system (TRCR) in this paper. We experiment with different machine learning algorithms, including a recently developed algorithm that is robust to biases in the training data, and show that our system retrieves table-related context with F1=0.735.

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Cited By

Zhang SBalog K(2020)Web Table Extraction, Retrieval, and AugmentationACM Transactions on Intelligent Systems and Technology10.1145/337211711:2(1-35)Online publication date: 25-Jan-2020
https://dl.acm.org/doi/10.1145/3372117
Yoshida MMatsumoto KKita K(2016)Table Topic Models for Hidden Unit EstimationInformation Retrieval Technology10.1007/978-3-319-48051-0_23(302-307)Online publication date: 15-Oct-2016
https://doi.org/10.1007/978-3-319-48051-0_23

Index Terms

Context Retrieval for Web Tables
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

ICTIR '15: Proceedings of the 2015 International Conference on The Theory of Information Retrieval

September 2015

402 pages

ISBN:9781450338332

DOI:10.1145/2808194

General Chairs:
James Allan
University of Massachusetts Amherst, USA
,
Bruce Croft
University of Massachusetts Amherst, USA
,
Program Chairs:
Arjen de Vries
CWI Amsterdam, The Netherlands
,
Chengxiang Zhai
University of Illinois at Urbana-Champaign, USA

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2015

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ICTIR '15

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SIGIR

ICTIR '15: ACM SIGIR International Conference on the Theory of Information Retrieval

September 27 - 30, 2015

Massachusetts, Northampton, USA

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ICTIR '15 Paper Acceptance Rate 29 of 57 submissions, 51%;

Overall Acceptance Rate 235 of 527 submissions, 45%

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Cited By

Zhang SBalog K(2020)Web Table Extraction, Retrieval, and AugmentationACM Transactions on Intelligent Systems and Technology10.1145/337211711:2(1-35)Online publication date: 25-Jan-2020
https://dl.acm.org/doi/10.1145/3372117
Yoshida MMatsumoto KKita K(2016)Table Topic Models for Hidden Unit EstimationInformation Retrieval Technology10.1007/978-3-319-48051-0_23(302-307)Online publication date: 15-Oct-2016
https://doi.org/10.1007/978-3-319-48051-0_23

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