FarsNewsQA: a deep learning-based question answering system for the Persian news articles

FarsNewsQA: a deep learning-based question answering system for the Persian news articles

Nowadays, a considerable volume of news articles is produced daily by news agencies worldwide. Since there is an extensive volume of news on the web, finding exact answers to the users’ questions is not a straightforward task. Developing Question Answering (QA) systems for the news articles can tack...

Full description

Saved in:
Bibliographic Details
Published in:Information retrieval (Boston) Vol. 26; no. 1-2; p. 3
Main Authors: Kazemi, Arefeh, Zojaji, Zahra, Malverdi, Mahdi, Mozafari, Jamshid, Ebrahimi, Fatemeh, Abadani, Negin, Varasteh, Mohammad Reza, Nematbakhsh, Mohammad Ali
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01-06-2023
Springer Nature B.V
Subjects:
Artificial intelligence
Complexity
Computer Science
Data Mining and Knowledge Discovery
Data Structures and Information Theory
Datasets
Deep learning
Domains
Information retrieval
Information Storage and Retrieval
Internet
Language
Linguistics
Natural language
Natural Language Processing (NLP)
Neural networks
News
Pattern Recognition
Persian language
Questions
Search engines
Deep learning
Persian
Question answering
News dataset
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Nowadays, a considerable volume of news articles is produced daily by news agencies worldwide. Since there is an extensive volume of news on the web, finding exact answers to the users’ questions is not a straightforward task. Developing Question Answering (QA) systems for the news articles can tackle this challenge. Due to the lack of studies on Persian QA systems and the importance and wild applications of QA systems in the news domain, this research aims to design and implement a QA system for the Persian news articles. This is the first attempt to develop a Persian QA system in the news domain to our best knowledge. We first create FarsQuAD: a Persian QA dataset for the news domain. We analyze the type and complexity of the users’ questions about the Persian news. The results show that What and Who questions have the most and Why and Which questions have the least occurrences in the Persian news domain. The results also indicate that the users usually raise complex questions about the Persian news. Then we develop FarsNewsQA: a QA system for answering questions about Persian news. We developed three models of the FarsNewsQA using BERT, ParsBERT, and ALBERT. The best version of the FarsNewsQA offers an F1 score of 75.61%, which is comparable with that of QA system on the English SQuAD dataset made by the Stanford university, and shows the new Bert-based technologies works well for Persian news QA systems.
ISSN:1386-4564
1573-7659
DOI:10.1007/s10791-023-09417-2