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Automatic adaptation of proper noun dictionaries through cooperation of machine learning and probabilistic methods

Authors:

Georgios Petasis,

Alessandro Cucchiarelli,

Georgios Paliouras,

Vangelis Karkaletsis,

Constantine D. SpyropoulosAuthors Info & Claims

SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

Pages 128 - 135

https://doi.org/10.1145/345508.345563

Published: 01 July 2000 Publication History

Abstract

The recognition of Proper Nouns (PNs) is considered an important task in the area of Information Retrieval and Extraction. However the high performance of most existing PN classifiers heavily depends upon the availability of large dictionaries of domain-specific Proper Nouns, and a certain amount of manual work for rule writing or manual tagging. Though it is not a heavy requirement to rely on some existing PN dictionary (often these resources are available on the web), its coverage of a domain corpus may be rather low, in absence of manual updating. In this paper we propose a technique for the automatic updating of an PN Dictionary through the cooperation of an inductive and a probabilistic classifier. In our experiments we show that, whenever an existing PN Dictionary allows the identification of 50% of the proper nouns within a corpus, our technique allows, without additional manual effort, the successful recognition of about 90% of the remaining 50%.

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Xu YTan XWang MZhang W(2024)GoalBERT: A Lightweight Named-Entity Recognition Model Based on Multiple FusionApplied Sciences10.3390/app14231100314:23(11003)Online publication date: 26-Nov-2024
https://doi.org/10.3390/app142311003
Park YYang GSohn CPark S(2024)GPDminer: a tool for extracting named entities and analyzing relations in biological literatureBMC Bioinformatics10.1186/s12859-024-05710-z25:1Online publication date: 6-Mar-2024
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Automatic adaptation of proper noun dictionaries through cooperation of machine learning and probabilistic methods

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cover image ACM Conferences

SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

July 2000

396 pages

ISBN:1581132263

DOI:10.1145/345508

Chairmen:
Emmanuel Yannakoudakis
Athens Univ. of Economics and Business, Greece
,
Nicholas J. Belkin
Rutgers Univ.
,
Mun-Kew Leong
Kent Ridge Digital Labs
,
Peter Ingwersen
Royal School of Library and Information Science

Copyright © 2000 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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Greek Com Soc: Greek Computer Society
SIGIR: ACM Special Interest Group on Information Retrieval
Athens U of Econ & Business: Athens University of Economics and Business

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Association for Computing Machinery

New York, NY, United States

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Published: 01 July 2000

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Greek Com Soc
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Athens U of Econ & Business

SIGIR00: 23rd ACM International SIGIR Conference on Research and Development in Information Retrieval

July 24 - 28, 2000

Athens, Greece

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Cited By

Xu YTan XWang MZhang W(2024)GoalBERT: A Lightweight Named-Entity Recognition Model Based on Multiple FusionApplied Sciences10.3390/app14231100314:23(11003)Online publication date: 26-Nov-2024
https://doi.org/10.3390/app142311003
Park YYang GSohn CPark S(2024)GPDminer: a tool for extracting named entities and analyzing relations in biological literatureBMC Bioinformatics10.1186/s12859-024-05710-z25:1Online publication date: 6-Mar-2024
https://doi.org/10.1186/s12859-024-05710-z
Refaat MRafea AGaballah N(2023)Named Entity Recognition From Biomedical Data2023 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI62032.2023.00141(838-844)Online publication date: 13-Dec-2023
https://doi.org/10.1109/CSCI62032.2023.00141
Parsaeimehr EFartash MAkbari Torkestani J(2023)Improving Feature Extraction Using a Hybrid of CNN and LSTM for Entity IdentificationNeural Processing Letters10.1007/s11063-022-11122-y55:5(5979-5994)Online publication date: 3-Jan-2023
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Ali SMasood KRiaz ASaud A(2022)Named Entity Recognition using Deep Learning: A Review2022 International Conference on Business Analytics for Technology and Security (ICBATS)10.1109/ICBATS54253.2022.9759051(1-7)Online publication date: 16-Feb-2022
https://doi.org/10.1109/ICBATS54253.2022.9759051
Wang YZhang CLi K(2022)A review on method entities in the academic literature: extraction, evaluation, and applicationScientometrics10.1007/s11192-022-04332-7127:5(2479-2520)Online publication date: 12-Mar-2022
https://doi.org/10.1007/s11192-022-04332-7
Li XWang TPang YHan JShi J(2022)Review of Research on Named Entity RecognitionAdvances in Artificial Intelligence and Security10.1007/978-3-031-06761-7_21(256-267)Online publication date: 8-Jul-2022
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Sharma AAmrita Chakraborty SKumar S(2021)Named Entity Recognition in Natural Language Processing: A Systematic ReviewProceedings of Second Doctoral Symposium on Computational Intelligence10.1007/978-981-16-3346-1_66(817-828)Online publication date: 20-Sep-2021
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Li JSun AHan JLi C(2020)A Survey on Deep Learning for Named Entity RecognitionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.2981314(1-1)Online publication date: 2020
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Bai YWang YXia BLi YZhu Z(2020)Adversarial Named Entity Recognition with POS label embedding2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207682(1-8)Online publication date: Jul-2020
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