Effect of Centrality Measures for Keyword Extraction from Turkish Documents

被引：0

作者：

Goz, Furkan ^{[1
]}

Mutlu, Alev ^{[1
]}

Kucuk, Kerem ^{[2
]}

Temur, Mahir ^{[3
]}

Gun, Abdurrahman ^{[1
]}

机构：

[1] Kocaeli Univ, Bilgisayar Muh Bolumu, Kocaeli, Turkey

[2] Kocaeli Univ, Yazilim Muh Bolumu, Kocaeli, Turkey

[3] Yapi Kredi Bankacil Ussu, Yapi Kredi Teknol, Kocaeli, Turkey

来源：

29TH IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS (SIU 2021) | 2021年

关键词：

automatic keyword extraction; graph centrality measures; Turkish document;

D O I：

10.1109/SIU53274.2021.9477807

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Keywords are salient words that best describe the content and the topic of a text document. As text documents are not usually annotated by their authors, automatic keyword extraction has become a challenging research topic. In this study, we investigate the performance of graph-centrality measures as initial node weights in graph-based keyword extraction on Tukish bank documents. To this aim, we focus on degree, eigenvector, betweenness, and closeness centralities and investigate their performance on keyword extraction on a 553 bank document dataset in Turkish. The experimental results show eigenvector centrality achieved the best results.

引用

页数：4

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