NDLI: Weighted Vote-Based Classifier Ensemble for Named Entity Recognition: A Genetic Algorithm-Based Approach

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Articulation-Disordered Speech Recognition Using Speaker-Adaptive Acoustic Models and Personalized Articulation Patterns

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Weighted Vote-Based Classifier Ensemble for Named Entity Recognition: A Genetic Algorithm-Based Approach

Visually and Phonologically Similar Characters in Incorrect Chinese Words: Analyses, Identification, and Applications

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Weighted Vote-Based Classifier Ensemble for Named Entity Recognition: A Genetic Algorithm-Based Approach

Content Provider	ACM Digital Library
Author	Ekbal, Asif Saha, Sriparna
Copyright Year	2011
Abstract	In this article, we report the search capability of Genetic Algorithm (GA) to construct a weighted vote-based classifier ensemble for Named Entity Recognition (NER). Our underlying assumption is that the reliability of predictions of each classifier differs among the various named entity (NE) classes. Thus, it is necessary to quantify the amount of voting of a particular classifier for a particular output class. Here, an attempt is made to determine the appropriate weights of voting for each class in each classifier using GA. The proposed technique is evaluated for four leading Indian languages, namely Bengali, Hindi, Telugu, and Oriya, which are all resource-poor in nature. Evaluation results yield the recall, precision and F-measure values of 92.08%, 92.22%, and 92.15%, respectively for Bengali; 96.07%, 88.63%, and 92.20%, respectively for Hindi; 78.82%, 91.26%, and 84.59%, respectively for Telugu; and 88.56%, 89.98%, and 89.26%, respectively for Oriya. Finally, we evaluate our proposed approach with the benchmark dataset of CoNLL-2003 shared task that yields the overall recall, precision, and $\textit{F}-measure$ values of 88.72%, 88.64%, and 88.68%, respectively. Results also show that the vote based classifier ensemble identified by the GA-based approach outperforms all the individual classifiers, three conventional baseline ensembles, and some other existing ensemble techniques. In a part of the article, we formulate the problem of feature selection in any classifier under the single objective optimization framework and show that our proposed classifier ensemble attains superior performance to it.
Starting Page	1
Ending Page	37
Page Count	37
File Format	PDF
ISSN	15300226
e-ISSN	15583430
DOI	10.1145/1967293.1967296
Volume Number	10
Issue Number	2
Journal	ACM Transactions on Asian Language Information Processing (TALIP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Language independent named entity recognition Classifier ensemble Conditional random field Feature selection Genetic algorithm Maximum entropy Support vector machine
Content Type	Text
Resource Type	Article
Subject	Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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