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Named entity recognition using a modified Pegasos algorithm

Authors:

Changki Lee,

Pum-Mo Ryu,

HyunKi KimAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 2337 - 2340

https://doi.org/10.1145/2063576.2063960

Published: 24 October 2011 Publication History

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Abstract

In this paper, we describe a named entity recognition using a modified Pegasos algorithm for structural SVMs. We show the modified Pegasos algorithm significantly outperformed CRFs and the training time for the modified Pegasos algorithm is reduced 17-26 times compared to CRFs.

References

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Cited By

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Park HOh KChoi HGweon G(2019)Constructing a paraphrase database for agglutinative languagesData & Knowledge Engineering10.1016/j.datak.2017.07.007123:COnline publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1016/j.datak.2017.07.007
LEE C(2017)LSTM-CRF Models for Named Entity RecognitionIEICE Transactions on Information and Systems10.1587/transinf.2016EDP7179E100.D:4(882-887)Online publication date: 2017
https://doi.org/10.1587/transinf.2016EDP7179
Han SKwon SYu HLee G(2017)Answer ranking based on named entity types for question answeringProceedings of the 11th International Conference on Ubiquitous Information Management and Communication10.1145/3022227.3022297(1-4)Online publication date: 5-Jan-2017
https://dl.acm.org/doi/10.1145/3022227.3022297
Show More Cited By

Index Terms

Named entity recognition using a modified Pegasos algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Named entities in text are persons, places, companies, etc. that are explicitly mentioned in text using proper nouns. The process of finding named entities in a text and classifying them to a semantic type, is called named entity recognition. Resolution ...
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We automatically create enormous, free and multilingual silver-standard training annotations for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner systems rely on statistical models of annotated data to identify ...

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Information

Published In

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

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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Association for Computing Machinery

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Published: 24 October 2011

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October 24 - 28, 2011

Glasgow, Scotland, UK

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Park HOh KChoi HGweon G(2019)Constructing a paraphrase database for agglutinative languagesData & Knowledge Engineering10.1016/j.datak.2017.07.007123:COnline publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1016/j.datak.2017.07.007
LEE C(2017)LSTM-CRF Models for Named Entity RecognitionIEICE Transactions on Information and Systems10.1587/transinf.2016EDP7179E100.D:4(882-887)Online publication date: 2017
https://doi.org/10.1587/transinf.2016EDP7179
Han SKwon SYu HLee G(2017)Answer ranking based on named entity types for question answeringProceedings of the 11th International Conference on Ubiquitous Information Management and Communication10.1145/3022227.3022297(1-4)Online publication date: 5-Jan-2017
https://dl.acm.org/doi/10.1145/3022227.3022297
Jung Y(2016)A semantic annotation framework for scientific publicationsQuality & Quantity10.1007/s11135-016-0369-351:3(1009-1025)Online publication date: 11-Jun-2016
https://doi.org/10.1007/s11135-016-0369-3
Piroi FLipani ALupu MHanbury A(2015)DASyR(IR) - document analysis system for systematic reviews (in Information Retrieval)Proceedings of the 2015 13th International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2015.7333830(591-595)Online publication date: 23-Aug-2015
https://dl.acm.org/doi/10.1109/ICDAR.2015.7333830
Mančev DTodorović B(2014)A primal sub-gradient method for structured classification with the averaged sum lossInternational Journal of Applied Mathematics and Computer Science10.2478/amcs-2014-006724:4(917-930)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.2478/amcs-2014-0067

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Two-stage approach to named entity recognition using Wikipedia and DBpedia

Named entity recognition and resolution in legal text

Learning multilingual named entity recognition from Wikipedia

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