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Classifier combination approach for question classification for Bengali question answering system

机译：孟加拉语问答系统中问题分类的分类器组合方法

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摘要

Question classification (QC) is a prime constituent of an automated question answering system. The work presented here demonstrates that a combination of multiple models achieves better classification performance than those obtained with existing individual models for the QC task in Bengali. We have exploited stateof-the-art multiple model combination techniques, i.e., ensemble, stacking and voting, to increase QC accuracy. Lexical, syntactic and semantic features of Bengali questions are used for four well-known classifiers, namelyNa?ve Bayes, kernel Na?¨ve Bayes, Rule Induction and Decision Tree, which serve as our base learners. Singlelayer question-class taxonomy with 8 coarse-grained classes is extended to two-layer taxonomy by adding 69 fine-grained classes. We carried out the experiments both on single-layer and two-layer taxonomies. Experimental results confirmed that classifier combination approaches outperform single-classifier classification approaches by 4.02% for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.
机译：问题分类（QC）是自动问题解答系统的主要组成部分。这里展示的工作表明，与使用现有单独模型进行的孟加拉国QC任务所获得的模型相比，多种模型的组合可实现更好的分类性能。我们已利用最新的多种模型组合技术（即合奏，堆叠和投票）来提高质量控制的准确性。孟加拉语问题的词汇，句法和语义特征被用于四个著名的分类器，即朴素贝叶斯，朴素贝叶斯核，规则归纳和决策树，它们是我们的基础学习者。通过添加69个细粒度类，将具有8个粗粒度类的单层问题类分类法扩展为两层分类法。我们对单层和两层分类法进行了实验。实验结果证实，对于粗粒度问题类别，分类器组合方法比单一分类器分类方法要好4.02％。总体而言，对于细粒度分类，堆叠方法可产生最佳结果，并达到87.79％的准确性。此处介绍的方法可以用在其他印度－雅利安语或印度语中，以开发问题回答系统。

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