Article

A search result clustering method using informatively named entities

Authors:

Ryoji KataokaAuthors Info & Claims

WIDM '05: Proceedings of the 7th annual ACM international workshop on Web information and data management

Pages 81 - 86

https://doi.org/10.1145/1097047.1097063

Published: 04 November 2005 Publication History

Abstract

Clustering the results of a search helps the user to overview the information returned. In this paper, we regard the clustering task as indexing the search results. Here, an index means a structured label list that can makes it easier for the user to comprehend the labels and search results. To realize this goal, we make three proposals. First is to use Named Entity Extraction for term extraction. Second is a new label selecting criterion based on importance in the search result and the relation between terms and search queries. The third is label categorization using category information of labels, which is generated by NE extraction. We implement a prototype system based on these proposals and find that it offers much higher performance than existing methods; we focus on news articles in this paper.

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Cited By

Shanmugavadivel KSubramanian MVasantharan KPrethish GSankar S(2024)Event Categorization from News Articles Using Machine Learning TechniquesSpeech and Language Technologies for Low-Resource Languages10.1007/978-3-031-58495-4_19(255-267)Online publication date: 24-Apr-2024
https://doi.org/10.1007/978-3-031-58495-4_19
Jibril ETantug A(2023)ANEC: An Amharic Named Entity Corpus and Transformer Based RecognizerIEEE Access10.1109/ACCESS.2023.324346811(15799-15815)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3243468
Chua HTucker-Kellogg LBhowmick S(2022)ArcheGEOProceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3535508.3545531(1-10)Online publication date: 7-Aug-2022
https://dl.acm.org/doi/10.1145/3535508.3545531
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Index Terms

A search result clustering method using informatively named entities
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

WIDM '05: Proceedings of the 7th annual ACM international workshop on Web information and data management

November 2005

96 pages

ISBN:1595931945

DOI:10.1145/1097047

Program Chairs:
Angela Bonifati
Icar CNR, Italy
,
Dongwon Lee
Penn State University, USA

Copyright © 2005 ACM.

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Published: 04 November 2005

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Cited By

Shanmugavadivel KSubramanian MVasantharan KPrethish GSankar S(2024)Event Categorization from News Articles Using Machine Learning TechniquesSpeech and Language Technologies for Low-Resource Languages10.1007/978-3-031-58495-4_19(255-267)Online publication date: 24-Apr-2024
https://doi.org/10.1007/978-3-031-58495-4_19
Jibril ETantug A(2023)ANEC: An Amharic Named Entity Corpus and Transformer Based RecognizerIEEE Access10.1109/ACCESS.2023.324346811(15799-15815)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3243468
Chua HTucker-Kellogg LBhowmick S(2022)ArcheGEOProceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3535508.3545531(1-10)Online publication date: 7-Aug-2022
https://dl.acm.org/doi/10.1145/3535508.3545531
Blanco-Gonzalez-Tejero CCano-Marin E(2022)Empowerment of women's entrepreneurship in family business through TwitterJournal of Family Business Management10.1108/JFBM-04-2022-005013:3(607-625)Online publication date: 29-Nov-2022
https://doi.org/10.1108/JFBM-04-2022-0050
Mottaghinia ZFeizi-Derakhshi MFarzinvash LSalehpour P(2020)A review of approaches for topic detection in TwitterJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2020.178501933:5(747-773)Online publication date: 28-Jun-2020
https://doi.org/10.1080/0952813X.2020.1785019
Sharma RMorwal SAgarwal BChandra RKhan M(2020)A deep neural network-based model for named entity recognition for Hindi languageNeural Computing and Applications10.1007/s00521-020-04881-zOnline publication date: 4-Apr-2020
https://doi.org/10.1007/s00521-020-04881-z
Larabi Marie-Sainte SAlalyani NAlotaibi SGhouzali SAbunadi I(2019)Arabic Natural Language Processing and Machine Learning-Based SystemsIEEE Access10.1109/ACCESS.2018.28900767(7011-7020)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2890076
Bansal RRani MKumar HKaushal S(2019)Temporal Information Retrieval and Its Application: A SurveyEmerging Research in Computing, Information, Communication and Applications10.1007/978-981-13-6001-5_19(251-262)Online publication date: 11-Sep-2019
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Alzboun STawalbeh SAl-Smadi MJararweh Y(2018)Using Bidirectional Long Short-Term Memory and Conditional Random Fields for Labeling Arabic Named Entities: A Comparative Study2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS)10.1109/SNAMS.2018.8554623(135-140)Online publication date: Oct-2018
https://doi.org/10.1109/SNAMS.2018.8554623
Gridach M(2018)Deep Learning Approach for Arabic Named Entity RecognitionComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-75477-2_31(439-451)Online publication date: 21-Mar-2018
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