research-article

Employing Semantic Context for Sparse Information Extraction Assessment

Authors:

Xindong WuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 12, Issue 5

Article No.: 54, Pages 1 - 36

https://doi.org/10.1145/3201407

Published: 27 June 2018 Publication History

Abstract

A huge amount of texts available on the World Wide Web presents an unprecedented opportunity for information extraction (IE). One important assumption in IE is that frequent extractions are more likely to be correct. Sparse IE is hence a challenging task because no matter how big a corpus is, there are extractions supported by only a small amount of evidence in the corpus. However, there is limited research on sparse IE, especially in the assessment of the validity of sparse IEs. Motivated by this, we introduce a lightweight, explicit semantic approach for assessing sparse IE.¹ We first use a large semantic network consisting of millions of concepts, entities, and attributes to explicitly model the context of any semantic relationship. Second, we learn from three semantic contexts using different base classifiers to select an optimal classification model for assessing sparse extractions. Finally, experiments show that as compared with several state-of-the-art approaches, our approach can significantly improve the F-score in the assessment of sparse extractions while maintaining the efficiency.

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Cited By

Liu YWu HHuang ZWang HNing YMa JLiu QChen E(2023)TechPat: Technical Phrase Extraction for Patent MiningACM Transactions on Knowledge Discovery from Data10.1145/359660317:9(1-31)Online publication date: 15-Jun-2023
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Algamdi SAlbanyan AShah STariq Z(2022)Twitter Accounts Suggestion: Pipeline Technique SpaCy Entity Recognition2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020570(5121-5125)Online publication date: 17-Dec-2022
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Index Terms

Employing Semantic Context for Sparse Information Extraction Assessment
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 12, Issue 5

October 2018

354 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3234931

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

Issue’s Table of Contents

Copyright © 2018 ACM.

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Publication History

Published: 27 June 2018

Accepted: 01 March 2018

Revised: 01 February 2018

Received: 01 September 2017

Published in TKDD Volume 12, Issue 5

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the Program for Changjiang Scholas and Innovative Research Team in University (PCSIRT) of the Ministry of Education
the Natural Science Foundation of China
the US National Science Foundation
National Key Research and Development Program of China
the Natural Science Foundation of Anhui province

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Cited By

Liu YWu HHuang ZWang HNing YMa JLiu QChen E(2023)TechPat: Technical Phrase Extraction for Patent MiningACM Transactions on Knowledge Discovery from Data10.1145/359660317:9(1-31)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1145/3596603
Wang YTong HZhu ZLi Y(2022)Nested Named Entity Recognition: A SurveyACM Transactions on Knowledge Discovery from Data10.1145/352259316:6(1-29)Online publication date: 30-Jul-2022
https://dl.acm.org/doi/10.1145/3522593
Algamdi SAlbanyan AShah STariq Z(2022)Twitter Accounts Suggestion: Pipeline Technique SpaCy Entity Recognition2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020570(5121-5125)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020570
Chen LTang XChen WQian YLi YZhang Y(2021)DACHA: A Dual Graph Convolution Based Temporal Knowledge Graph Representation Learning Method Using Historical RelationACM Transactions on Knowledge Discovery from Data10.1145/347705116:3(1-18)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.1145/3477051
Chen XJin LZhu YLuo CWang T(2021)Text Recognition in the WildACM Computing Surveys10.1145/344075654:2(1-35)Online publication date: 5-Mar-2021
https://dl.acm.org/doi/10.1145/3440756
Hair JSarstedt M(2021)Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketingJournal of Marketing Theory and Practice10.1080/10696679.2020.1860683(1-13)Online publication date: 11-Jan-2021
https://doi.org/10.1080/10696679.2020.1860683
Zhong LZhu Y(2020)Relation Extraction with Proactive Domain Adaptation Strategy2020 IEEE International Conference on Knowledge Graph (ICKG)10.1109/ICBK50248.2020.00069(441-448)Online publication date: Aug-2020
https://doi.org/10.1109/ICBK50248.2020.00069
Asgari-Bidhendi MHadian AMinaei-Bidgoli BKejriwal MLopez VSequeda J(2019)FarsBaseSemantic Web10.3233/SW-19036910:6(1169-1196)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/SW-190369
Adnan KAkbar R(2019)Limitations of information extraction methods and techniques for heterogeneous unstructured big dataInternational Journal of Engineering Business Management10.1177/184797901989077111Online publication date: 9-Dec-2019
https://doi.org/10.1177/1847979019890771
Wanjawa BMuchemi L(2018)Automatic Semantic Network Generation from Unstructured Documents – The Options2018 5th International Conference on Soft Computing & Machine Intelligence (ISCMI)10.1109/ISCMI.2018.8703225(72-78)Online publication date: Nov-2018
https://doi.org/10.1109/ISCMI.2018.8703225

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