Building a benchmark dataset for the Kurdish news question answering

被引：0
|
作者：
Saeed, Ari M. [1 ]
机构：
[1] Univ Halabja, Coll Sci, Comp Sci Dept, Halabja, Kurdistan Regio, Iraq
来源：
DATA IN BRIEF | 2024年 / 57卷
关键词：
Kurdish question answering system; Kurdish news dataset; Data mining; Text pre-processing; Machine learning;
D O I：
10.1016/j.dib.2024.110916
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
This article presents the Kurdish News Question Answering Dataset (KNQAD). The texts are collected from various Kurdish news websites. The ParsHub software is used to extract data from different fields of news, such as social news, religion, sports, science, and economy. The dataset consists of 15,002 news paragraphs with question-answer pairs. For each news paragraph, one or more question-answer pairs are manually created based on the content of the paragraphs. The dataset is pre-processed by cleaning and normalizing the data. During the cleaning process, special characters and stop words are removed, and stemming is used as a normalization step. The distribution of each question type is presented in the KNQAD. Moreover, the complexity of the QA problem is analyzed in the KNQAD by using lexical similarity techniques between questions and answers. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ )
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页数：12
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