short-paper

Corpus-based Set Expansion with Lexical Features and Distributed Representations

Authors:

James AllanAuthors Info & Claims

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1153 - 1156

https://doi.org/10.1145/3331184.3331359

Published: 18 July 2019 Publication History

Abstract

Corpus-based set expansion refers to mining "sibling" entities of some given seed entities from a corpus. Previous works are limited to using either textual context matching or semantic matching to fulfill this task. Neither matching method takes full advantage of the rich information in free text. We present CaSE, an efficient unsupervised corpus-based set expansion framework that leverages lexical features as well as distributed representations of entities for the set expansion task. Experiments show that CaSE outperforms state-of-the-art set expansion algorithms in terms of expansion accuracy.

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Cited By

Zhang XChen GCui SSheng JLiu TXu HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Exogenous and Endogenous Data Augmentation for Low-Resource Complex Named Entity RecognitionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657754(630-640)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657754
Yu PRahimi RHuang ZAllan JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Search Result Diversification Using Query Aspects as BottlenecksProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615050(3040-3051)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615050
Li YHuang SZhang XZhou QLi YLiu RCao YZheng HShen Y(2023)Automatic Context Pattern Generation for Entity Set ExpansionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327521135:12(12458-12469)Online publication date: 1-Dec-2023
https://doi.org/10.1109/TKDE.2023.3275211
Show More Cited By

Index Terms

Corpus-based Set Expansion with Lexical Features and Distributed Representations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction

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cover image ACM Conferences

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2019

1512 pages

ISBN:9781450361729

DOI:10.1145/3331184

General Chairs:
Benjamin Piwowarski
CNRS - Sorbonne Universite, France
,
Max Chevalier
Universite de Toulouse, CNRS, France
,
Eric Gaussier
Universite Grenoble Alpes, CNRS, France
,
Program Chairs:
Yoelle Maarek
Amazon Research, Israel
,
Jian-Yun Nie
University of Montreal, Canada
,
Falk Scholer
RMIT University, Australia

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

Publication History

Published: 18 July 2019

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SIGIR

SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 21 - 25, 2019

Paris, France

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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Zhang XChen GCui SSheng JLiu TXu HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Exogenous and Endogenous Data Augmentation for Low-Resource Complex Named Entity RecognitionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657754(630-640)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657754
Yu PRahimi RHuang ZAllan JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Search Result Diversification Using Query Aspects as BottlenecksProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615050(3040-3051)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615050
Li YHuang SZhang XZhou QLi YLiu RCao YZheng HShen Y(2023)Automatic Context Pattern Generation for Entity Set ExpansionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327521135:12(12458-12469)Online publication date: 1-Dec-2023
https://doi.org/10.1109/TKDE.2023.3275211
Negreanu CKaraoglu AWilliams JChen SFabian DGordon ALin C(2022)Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language ModelCompanion Proceedings of the Web Conference 202210.1145/3487553.3524923(1272-1280)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524923
Li YLi YHe YYu TShen YZheng HAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Contrastive Learning with Hard Negative Entities for Entity Set ExpansionProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531954(1077-1086)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531954
Zhang YZhang YJiang YMichalski MDeng YPopa LZhai CHan J(2022)Entity Set Co-Expansion in StackOverflow2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020770(4792-4795)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020770
Shen JHan JShen JHan J(2022)Concept Set ExpansionAutomated Taxonomy Discovery and Exploration10.1007/978-3-031-11405-2_2(9-29)Online publication date: 22-Sep-2022
https://doi.org/10.1007/978-3-031-11405-2_2
Wahed MGruhl DAlba AGentile ARistoski PDeLuca CWelch SLourentzou IDemartini GZuccon GCulpepper JHuang ZTong H(2021)SAUCEProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481950(4173-4183)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481950
Huang ZRahimi RYu PShang JAllan JDiaz FShah CSuel TCastells PJones RSakai T(2021)AutoName: A Corpus-Based Set Naming FrameworkProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463100(2101-2105)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463100
Ota MMüller HFreire JSrivastava D(2020)Data-driven domain discovery for structured datasetsProceedings of the VLDB Endowment10.14778/3384345.338434613:7(953-967)Online publication date: 26-Mar-2020
https://dl.acm.org/doi/10.14778/3384345.3384346

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