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A high-performance semi-supervised learning method for text chunking

Authors:

Rie Kubota Ando,

Tong ZhangAuthors Info & Claims

ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

Pages 1 - 9

https://doi.org/10.3115/1219840.1219841

Published: 25 June 2005 Publication History

Abstract

In machine learning, whether one can build a more accurate classifier by using unlabeled data (semi-supervised learning) is an important issue. Although a number of semi-supervised methods have been proposed, their effectiveness on NLP tasks is not always clear. This paper presents a novel semi-supervised method that employs a learning paradigm which we call structural learning. The idea is to find "what good classifiers are like" by learning from thousands of automatically generated auxiliary classification problems on unlabeled data. By doing so, the common predictive structure shared by the multiple classification problems can be discovered, which can then be used to improve performance on the target problem. The method produces performance higher than the previous best results on CoNLL'00 syntactic chunking and CoNLL'03 named entity chunking (English and German).

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A high-performance semi-supervised learning method for text chunking
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ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

June 2005

657 pages

General Chair:
Kevin Knight
University of Southern California

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 25 June 2005

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ACL '05 Paper Acceptance Rate 77 of 423 submissions, 18%;

Overall Acceptance Rate 85 of 443 submissions, 19%

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Cited By

Zheng ZWang RTao ZLi HChen CLi TGuo S(2024)Automated patch correctness predicting to fix software defectExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124877256:COnline publication date: 5-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124877
Walambe RMarathe AKotecha KGhinea G(2021)Lightweight Object Detection Ensemble Framework for Autonomous Vehicles in Challenging Weather ConditionsComputational Intelligence and Neuroscience10.1155/2021/52788202021Online publication date: 1-Jan-2021
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Yu XChu YJiang FGuo YGong D(2018)SVMs Classification Based Two-side Cross Domain Collaborative Filtering by inferring intrinsic user and item featuresKnowledge-Based Systems10.1016/j.knosys.2017.11.010141:C(80-91)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.knosys.2017.11.010
Fan XLuo WMenekse MLitman DWang JPapadopoulos GKuflik TChen FDuarte CFu W(2017)Scaling Reflection Prompts in Large Classrooms via Mobile Interfaces and Natural Language ProcessingProceedings of the 22nd International Conference on Intelligent User Interfaces10.1145/3025171.3025204(363-374)Online publication date: 7-Mar-2017
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Tencer LReznakova MCheriet M(2017)Summit-TrainingApplied Soft Computing10.1016/j.asoc.2016.06.00850:C(1-20)Online publication date: 1-Jan-2017
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Tencer LReznakova MCheriet M(2017)UFuzzyApplied Soft Computing10.1016/j.asoc.2016.05.04152:C(1296-1315)Online publication date: 1-Mar-2017
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Beheshti SBenatallah BVenugopal SRyu SMotahari-Nezhad HWang W(2017)A systematic review and comparative analysis of cross-document coreference resolution methods and toolsComputing10.1007/s00607-016-0490-099:4(313-349)Online publication date: 1-Apr-2017
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Bhatt HRajkumar ARoy S(2016)Multi-source iterative adaptation for cross-domain classificationProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3061053.3061135(3691-3697)Online publication date: 9-Jul-2016
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Chou CChang CHuang Y(2016)Boosted Web Named Entity Recognition via Tri-TrainingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/296310016:2(1-23)Online publication date: 14-Oct-2016
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