BERT ile Kazak Haber Veri Kümesinden Anahtar Kelime Çıkarımı-Reference-Cited by-同舟云学术

BERT ile Kazak Haber Veri Kümesinden Anahtar Kelime Çıkarımı

Published:2022-09-07 Issue: Volume: Page:
ISSN:2148-3736
Container-title:El-Cezeri Fen ve Mühendislik Dergisi
language:en
Short-container-title:ECJSE

Author:

ABİBULLAYEVA Aiman¹,ÇETİN Aydın¹

Affiliation:

1. GAZI UNIVERSITY, FACULTY OF TECHNOLOGY, DEPARTMENT OF COMPUTER ENGINEERING

Abstract

Keywords provide a concise and precise description of the document's content. Due to the importance of the keyword and the difficulty of manual markup, automatic keyword extraction makes this process easy and fast. In this paper, Keyword Extraction from Kazakh News Dataset was presented. Model performance results were obtained by using the BERT base - uncased and BERT-base-multilingual-uncased pre-trained language model for the newly compiled Kazakh News Dataset-KND. Compiled Kazakh news data set consists of 7060 data. Data were collected from the web pages anatili.kazgazeta.kz, Bilimdinews.kz, and zhasalash.kz using the BeautifulSoap and Requests libraries. These web pages mostly contain news, history, and literary texts. The dataset includes the publication name or news title, the author of the publication or news subject, and the URL of the Kazakh news site. In the evaluation of the training results, it was observed that the BERT base-multilingual-uncased F-score performance was higher than the BERT model.

Publisher

El-Cezeri: Journal of Science and Engineering

Subject

General Physics and Astronomy,General Engineering,General Chemical Engineering,General Chemistry,General Computer Science

Reference14 articles.

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