面向伪问答系统的孟加拉语问题分类

Syeda Tamanna Alam Monisha, Sourav Sarker, Md Mahadi Hasan Nahid
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引用次数: 6

摘要

本文对孟加拉语问题进行了问题分类,这是开发基于封闭域的孟加拉语事实问答系统的第一步。问题分类是问答系统的重要一步,整个系统的性能取决于问题分类。我们使用了机器学习算法随机梯度下降(SGD)、决策树(DT)、支持向量机(SVM)和朴素贝叶斯(NB)进行问题分类过程,其中具有线性核的支持向量机(SVM)表现最好,在五个粗粒度类别中准确率为90.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Bengali Questions Towards a Factoid Question Answering System
This paper demonstrates question classification for Bengali language questions which is the first step towards developing a closed domain based Bengali factoid question answering system. Question classification is an influential step towards a question answering system on which the performance of the whole system depends. We have used machine learning algorithms Stochastic Gradient Descent(SGD), Decision Tree(DT), Support Vector Machine(SVM) and Naive Bayes(NB) for the question classification process where Support Vector Machine(SVM) with linear kernel performs the best providing the accuracy 90.6% over five coarse-grained categories.
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