The Effect of Data Augmentation Techniques on Persian Stance Detection

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Farhoodi Mojgan; Toloie Eshlaghi Abbas; Motadel Mohamadreza

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: International Journal of Information and Communication Technology Research Year:2023 | Volume:15 | Issue:1 Page(s): 63-71

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

The Effect of Data Augmentation Techniques on Persian Stance Detection

Author(s)

Farhoodi Mojgan | Toloie Eshlaghi Abbas | Motadel Mohamadreza | Issue Writer Certificate

Keywords

stance detection

data augmentation.fake news

dataset

Abstract

The purpose of stance detection is to identify the author's stance toward a particular topic or claim. stance detection has become a key component in applications such as fake news detection, claim validation, argument searching, and author profiling. Although significant progress has been made in stance detection in languages such as English, little attention has been paid in some other languages, including Persian. One of the main problems of research in Persian stance detection is the shortage of appropriate datasets. In this article, to address this problem, we consider data augmentation, the artificial creation of training data, which is used to conquer the shortage of datasets. In this research, we studied several methods of data augmentation such as EDA, back-translation, and merging source dataset with similar one in English language. The experimental results indicate that combining the primary data set with the translation of another dataset with similar content in another language (for example English) result in a significant improvement in the performance of the model.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans