International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Developing and Pre-Processing a Dataset using a Rhetorical Relation to Build a Question-Answering System based on an Unsupervised Learning Approach

  • Dutta, Ashit Kumar (Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University) ;
  • Wahab sait, Abdul Rahaman (Center of Documents and Archive, King Faisal University) ;
  • Keshta, Ismail Mohamed (Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University) ;
  • Elhalles, Abheer (Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University)
  • Received : 2021.11.05
  • Published : 2021.11.30
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Abstract

Rhetorical relations between two text fragments are essential information and support natural language processing applications such as Question - Answering (QA) system and automatic text summarization to produce an effective outcome. Question - Answering (QA) system facilitates users to retrieve a meaningful response. There is a demand for rhetorical relation based datasets to develop such a system to interpret and respond to user requests. There are a limited number of datasets for developing an Arabic QA system. Thus, there is a lack of an effective QA system in the Arabic language. Recent research works reveal that unsupervised learning can support the QA system to reply to users queries. In this study, researchers intend to develop a rhetorical relation based dataset for implementing unsupervised learning applications. A web crawler is developed to crawl Arabic content from the web. A discourse-annotated corpus is generated using the rhetorical structural theory. A Naïve Bayes based QA system is developed to evaluate the performance of datasets. The outcome shows that the performance of the QA system is improved with proposed dataset and able to answer user queries with an appropriate response. In addition, the results on fine-grained and coarse-grained relations reveal that the dataset is highly reliable.

Keywords

Acknowledgement

The authors would like to acknowledge the support provided by AlMaarefa University while conducting this research work.

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