Article

Unsupervised named-entity recognition: generating gazetteers and resolving ambiguity

Authors:

Peter D. Turney,

Stan MatwinAuthors Info & Claims

AI'06: Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence

Pages 266 - 277

https://doi.org/10.1007/11766247_23

Published: 07 June 2006 Publication History

Abstract

In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system's architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands).

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Cited By

Walshe TSimpson A(2023)Towards a Greater Understanding of Coordinated Vulnerability Disclosure Policy DocumentsDigital Threats: Research and Practice10.1145/35861804:2(1-36)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3586180
Niewiadomski SMzyk G(2023)ML Support for Conformity Checks in CMDB-Like DatabasesArtificial Intelligence and Soft Computing10.1007/978-3-031-42508-0_33(366-376)Online publication date: 18-Jun-2023
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Impedovo ABarracchia ERizzo G(2022)Exploiting Named Entity Recognition for Information Extraction from Italian Procurement Documents: A Case StudyInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_5(60-74)Online publication date: 28-Nov-2022
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Published In

cover image Guide Proceedings

AI'06: Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence

June 2006

561 pages

ISBN:3540346287

Editors:
Luc Lamontagne
Departement of Computer Science and Software Engineering, Laval University, Québec, Canada
,
Mario Marchand
Département IFT-GLO, Pavillon Adrien-Pouliot, Université Laval, Québec, Canada

Sponsors

CSCSI: Canadian Society for the Computional Studies of Intelligence
SCEIO: Soc can pour l'etude de l'intelligence par ordinateur

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 June 2006

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Cited By

Walshe TSimpson A(2023)Towards a Greater Understanding of Coordinated Vulnerability Disclosure Policy DocumentsDigital Threats: Research and Practice10.1145/35861804:2(1-36)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3586180
Niewiadomski SMzyk G(2023)ML Support for Conformity Checks in CMDB-Like DatabasesArtificial Intelligence and Soft Computing10.1007/978-3-031-42508-0_33(366-376)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1007/978-3-031-42508-0_33
Impedovo ABarracchia ERizzo G(2022)Exploiting Named Entity Recognition for Information Extraction from Italian Procurement Documents: A Case StudyInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_5(60-74)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-21047-1_5
Nasar ZJaffry SMalik M(2021)Named Entity Recognition and Relation ExtractionACM Computing Surveys10.1145/344596554:1(1-39)Online publication date: 11-Feb-2021
https://dl.acm.org/doi/10.1145/3445965
Hanh TDoucet ASidere NMoreno JPollak S(2021)Named Entity Recognition Architecture Combining Contextual and Global FeaturesTowards Open and Trustworthy Digital Societies10.1007/978-3-030-91669-5_21(264-276)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/978-3-030-91669-5_21
Karampatakis SDimitriadis ARevenko ABlaschke C(2020)Training NER Models: Knowledge Graphs in the LoopThe Semantic Web: ESWC 2020 Satellite Events10.1007/978-3-030-62327-2_23(135-139)Online publication date: 31-May-2020
https://dl.acm.org/doi/10.1007/978-3-030-62327-2_23
Xu HChen YSun JCao XXie R(2020)Iterative Strategy for Named Entity Recognition with Imperfect AnnotationsNatural Language Processing and Chinese Computing10.1007/978-3-030-60457-8_42(512-523)Online publication date: 14-Oct-2020
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Liao XZhao Z(2019)Unsupervised Approaches for Textual Semantic Annotation, A SurveyACM Computing Surveys10.1145/332447352:4(1-45)Online publication date: 30-Aug-2019
https://dl.acm.org/doi/10.1145/3324473
Štravs MZupančič J(2019)Named Entity Recognition Using Gazetteer of Hierarchical EntitiesAdvances and Trends in Artificial Intelligence. From Theory to Practice10.1007/978-3-030-22999-3_65(768-776)Online publication date: 9-Jul-2019
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Gabbard RDeyoung JLignos CFreedman MWeischedel R(2018)Combining rule-based and statistical mechanisms for low-resource named entity recognitionMachine Translation10.1007/s10590-017-9208-032:1-2(31-43)Online publication date: 1-Jun-2018
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