Article

Using term informativeness for named entity detection

Authors:

Jason D. M. Rennie,

Tommi JaakkolaAuthors Info & Claims

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 353 - 360

https://doi.org/10.1145/1076034.1076095

Published: 15 August 2005 Publication History

Abstract

Informal communication (e-mail, bulletin boards) poses a difficult learning environment because traditional grammatical and lexical information are noisy. Other information is necessary for tasks such as named entity detection. How topic-centric, or informative, a word is can be valuable information. It is well known that informative words are best modeled by "heavy-tailed" distributions, such as mixture models. However, informativeness scores do not take full advantage of this fact. We introduce a new informativeness score that directly utilizes mixture model likelihood to identify informative words. We use the task of extracting restaurant names from bulletin board posts as a way to determine effectiveness. We find that our "mixture score" is weakly effective alone and highly effective when combined with Inverse Document Frequency. We compare against other informativeness criteria and find that only Residual IDF is competitive against our combined IDF/Mixture score.

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Li RYamakata YTajima K(2022)Active Learning Strategies Based on Text Informativeness2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00015(32-39)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00015
Elia AMaisto AMartorelli GPelosi SVitale P(2020)Textual Statistics and Named Entity Recognition Applied to Game of Thrones NovelsWeb, Artificial Intelligence and Network Applications10.1007/978-3-030-44038-1_20(214-222)Online publication date: 31-Mar-2020
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Index Terms

Using term informativeness for named entity detection
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning

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Published In

cover image ACM Conferences

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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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: 15 August 2005

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SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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

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Cited By

Azib MRenard BGarnier PGénot VAndré N(2023)A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00211(1399-1404)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICMLA58977.2023.00211
Li RYamakata YTajima K(2022)Active Learning Strategies Based on Text Informativeness2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00015(32-39)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00015
Elia AMaisto AMartorelli GPelosi SVitale P(2020)Textual Statistics and Named Entity Recognition Applied to Game of Thrones NovelsWeb, Artificial Intelligence and Network Applications10.1007/978-3-030-44038-1_20(214-222)Online publication date: 31-Mar-2020
https://doi.org/10.1007/978-3-030-44038-1_20
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Bhattacharya IFoley MZhang NZhang TKu CMine CJi HRiedl CWelles BRadke RD'Mello SGeorgiou PScherer SProvost ESoleymani MWorsley M(2018)A Multimodal-Sensor-Enabled Room for Unobtrusive Group Meeting AnalysisProceedings of the 20th ACM International Conference on Multimodal Interaction10.1145/3242969.3243022(347-355)Online publication date: 2-Oct-2018
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Khairova NLewoniewski WWęcel KOrken MKuralai M(2018)Comparative Analysis of the Informativeness and Encyclopedic Style of the Popular Web Information SourcesBusiness Information Systems10.1007/978-3-319-93931-5_24(333-344)Online publication date: 16-Jun-2018
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Shirakawa MHara TNishio S(2017)IDF for Word N-gramsACM Transactions on Information Systems10.1145/305277536:1(1-38)Online publication date: 5-Jun-2017
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Johnson AGhassemi MNemati SNiehaus KClifton DClifford G(2016)Machine Learning and Decision Support in Critical CareProceedings of the IEEE10.1109/JPROC.2015.2501978104:2(444-466)Online publication date: Feb-2016
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Zong NLee SKim H(2015)Discovering expansion entities for keyword-based entity search in linked dataJournal of Information Science10.1177/016555151456270441:2(209-227)Online publication date: 6-Jan-2015
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