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Weighted Vote Based Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition

机译：基于遗传算法的加权投票分类器集合选择

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摘要

In this paper, 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. Weights of voting should be high for the NE classes for which the classifier is most reliable and low for the NE classes for which the classifier is not at all reliable. Here, an attempt is made to quantify the amount of voting for each class in each classifier using GA. We use Maximum Entropy (ME) framework to build a number of classifiers depending upon the various representations of a set of features, language independent in nature. The proposed technique is evaluated with two resource-constrained languages, namely Bengali and Hindi. Evaluation results yield the recall, precision and F-measure values of 73.81%, 84.92% and 78.98%, respectively for Bengali and 65.12%, 82.03% and 72.60%, respectively for Hindi. Results also show that the proposed weighted vote based classifier ensemble identified by GA outperforms all the individual classifiers and three conventional baseline ensemble techniques for both the languages.
机译：在本文中，我们报告了遗传算法（GA）的搜索能力，以构建基于加权投票的分类器集成，用于命名实体识别（NER）。我们的基本假设是，每个分类器的预测可靠性在各种命名实体（NE）类之间是不同的。对于分类器最可靠的NE类，表决权应该高，而对于分类器根本不可靠的NE类，表决权应低。在此，尝试使用GA量化每个分类器中每个类别的投票量。我们使用最大熵（ME）框架根据一组功能的各种表示来构建许多分类器，这些功能本质上与语言无关。使用两种资源受限的语言（孟加拉语和北印度语）对所提出的技术进行了评估。评估结果显示，孟加拉语的召回率，精确度和F测量值分别为孟加拉国73.81％，84.92％和78.98％，印地语分别为65.12％，82.03％和72.60％。结果还表明，由GA识别的拟议基于加权投票的分类器集成优于两种语言的所有单个分类器和三种常规基线集成技术。

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