research-article

An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation

Authors:

Chin-Yew LinAuthors Info & Claims

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 635 - 644

https://doi.org/10.1145/2766462.2767698

Published: 09 August 2015 Publication History

Abstract

This paper studies Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to handle unseen entities without annotation. A baseline solution is to build a global entity-unspecific model for all entities regardless of the relationship information among entities, which cannot guarantee to achieve satisfactory result for each entity. In this paper, we propose a novel entity class-dependent discriminative mixture model by introducing a latent entity class layer to model the correlations between entities and latent entity classes. The model can better adjust to different types of entities and achieve better performance when dealing with a broad range of entities. An extensive set of experiments has been conducted on TREC-KBA-2013 dataset, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.

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Cited By

Xu JYu JCai YChua T(2024)Dual Contrastive Learning for Cross-Domain Named Entity RecognitionACM Transactions on Information Systems10.1145/367887942:6(1-33)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3678879
Ostapuk NDifallah DCudre-Mauroux P(2022)ParaGraph: Mapping Wikidata Tail Entities to Wikipedia Paragraphs2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020207(6008-6017)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020207
Ali ZUllah IKhan AUllah Jan AMuhammad K(2021)An overview and evaluation of citation recommendation modelsScientometrics10.1007/s11192-021-03909-yOnline publication date: 2-Mar-2021
https://doi.org/10.1007/s11192-021-03909-y
Show More Cited By

Index Terms

An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2015

1198 pages

ISBN:9781450336215

DOI:10.1145/2766462

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

Copyright © 2015 ACM.

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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: 09 August 2015

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Funding Sources

National Natural Science Foundation of China
National Program on Key Basic Research Project(973 Program)
Beijing Higher Education Young Elite Teacher Project

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SIGIR '15

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SIGIR

SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;

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

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Cited By

Xu JYu JCai YChua T(2024)Dual Contrastive Learning for Cross-Domain Named Entity RecognitionACM Transactions on Information Systems10.1145/367887942:6(1-33)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3678879
Ostapuk NDifallah DCudre-Mauroux P(2022)ParaGraph: Mapping Wikidata Tail Entities to Wikipedia Paragraphs2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020207(6008-6017)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020207
Ali ZUllah IKhan AUllah Jan AMuhammad K(2021)An overview and evaluation of citation recommendation modelsScientometrics10.1007/s11192-021-03909-yOnline publication date: 2-Mar-2021
https://doi.org/10.1007/s11192-021-03909-y
Ma LSong DLiao LWang J(2020)A Hybrid Discriminative Mixture Model for Cumulative Citation RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289332832:4(617-630)Online publication date: 1-Apr-2020
https://doi.org/10.1109/TKDE.2019.2893328
Ma SZhang CLiu X(2020)A review of citation recommendation: from textual content to enriched contextScientometrics10.1007/s11192-019-03336-0Online publication date: 3-Jan-2020
https://doi.org/10.1007/s11192-019-03336-0
Alkhatib WRensing C(2020)Personalized Citation Recommendation Using an Ensemble Model of DSSM and Bibliographic InformationArtificial Intelligence Supported Educational Technologies10.1007/978-3-030-41099-5_10(175-192)Online publication date: 30-Apr-2020
https://doi.org/10.1007/978-3-030-41099-5_10
Cai XHan JLi WZhang RPan SYang L(2018)A Three-Layered Mutually Reinforced Model for Personalized Citation RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2018.281724529:12(6026-6037)Online publication date: Dec-2018
https://doi.org/10.1109/TNNLS.2018.2817245
Balog KBalog K(2018)Populating Knowledge BasesEntity-Oriented Search10.1007/978-3-319-93935-3_6(189-222)Online publication date: 3-Oct-2018
https://doi.org/10.1007/978-3-319-93935-3_6
Reinanda RMeij Ede Rijke MMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Document Filtering for Long-tail EntitiesProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983728(771-780)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983728
Wang JJiang JLiao LSong DZhang ZLin C(2016)Cold Start Cumulative Citation Recommendation for Knowledge Base AccelerationAdvances in Information Retrieval10.1007/978-3-319-30671-1_63(748-753)Online publication date: 2016
https://doi.org/10.1007/978-3-319-30671-1_63

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