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Learning to Extract Comparison Points of Entity Pairs from Wikipedia Articles

Authors:

Sandeep Kumar Pani,

Naresh R.,

Pawan Goyal,

Plaban Kumar BhowmickAuthors Info & Claims

JCDL '18: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries

Pages 373 - 374

https://doi.org/10.1145/3197026.3203909

Published: 23 May 2018 Publication History

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Abstract

In this paper, we present preliminary results on a novel task of extracting comparison points for a pair of entities from the text articles describing them. The task is challenging as comparison points in a typical pair of articles tend to be sparse. We presented a multi-level document analysis (viz. document, paragraph and sentence level) for extracting the comparisons. For extracting sentence level comparisons, which is the hardest task among three, we have used Convolutional Neural Network (CNN) with features extracted around <entity, aspect, value> triple. Experiments conducted on a small dataset provide encouraging performance.

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Index Terms

Learning to Extract Comparison Points of Entity Pairs from Wikipedia Articles
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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Published In

JCDL '18: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries

May 2018

453 pages

ISBN:9781450351782

DOI:10.1145/3197026

General Chairs:
Jiangping Chen
College of Information, UNT, USA
,
Marcos André Gonçalves
, Brazil
,
Jeff M. Allen
College of Information, UNT, USA
,
Program Chairs:
Edward A. Fox
Virginia Tech, USA
,
Min-Yen Kan
National University of Singapore, Singapore
,
Vivien Petras
Humboldt-Universität zu Berlin, Germany

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 23 May 2018

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JCDL '18

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JCDL '18: The 18th ACM/IEEE Joint Conference on Digital Libraries

June 3 - 7, 2018

Texas, Fort Worth, USA

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JCDL '18 Paper Acceptance Rate 26 of 71 submissions, 37%;

Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Comparison of entity with fuzzy data types in fuzzy object-oriented databases

Hybrid method to automatically extract medical document tree structure

A Comparison of Approaches for Measuring Cross-Lingual Similarity of Wikipedia Articles

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