Miah, Mohammad Badrul Alam and Suryanti, Awang and Rahman, Md Mustafizur and Sanwar Hosen, A. S. M. (2023) Keyphrase distance analysis technique from news articles as a feature for keyphrase extraction: An unsupervised approach. International Journal of Advanced Computer Science and Applications (IJACSA), 14 (10). pp. 995-1002. ISSN 2156-5570(Online). (Published)
|
Pdf
Keyphrase distance analysis technique from news articles as a feature for keyphrase extraction.pdf Available under License Creative Commons Attribution. Download (845kB) | Preview |
Abstract
Due to the rapid expansion of information and online sources, automatic keyphrase extraction remains an important and challenging problem in the field of current study. The use of keyphrases is extremely beneficial for many tasks, including information retrieval (IR) systems and natural language processing (NLP). It is essential to extract the features of those keyphrases for extracting the most significant keyphrases as well as summarizing the texts to the highest standard. In order to analyze the distance between keyphrases in news articles as a feature of keyphrases, this research proposed a region-based unsupervised keyphrase distance analysis (KDA) technique. The proposed method is broken down into eight steps: gathering data, data preprocessing, data processing, searching keyphrases, distance calculation, averaging distance, curve plotting, and lastly, the curve fitting technique. The proposed approach begins by gathering two distinct datasets containing the news items, which are then used in the data preprocessing step, which makes use of a few preprocessing techniques. This preprocessed data is then employed in the data processing phase, where it is routed to the keyphrase searching, distance computation, and distance averaging phases. Finally, the curve fitting method is used after applying a curve plotting analysis. These two benchmark datasets are then used to evaluate and test the performance of the proposed approach. The proposed approach is then contrasted with different approaches to show how effective, advantageous, and significant it is. The results of the evaluation also proved that the proposed technique considerably improved the efficiency of keyphrase extraction techniques. It produces an F1-score value of 96.91% whereas its present keyphrases are 94.55%.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Curve fitting technique; Data pre-processing; Data processing; Feature extraction; KDA technique; Keyphrase extraction |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing Faculty of Mechanical and Automotive Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 31 Oct 2023 01:50 |
| Last Modified: | 05 Jan 2024 07:41 |
| URI: | http://umpir.ump.edu.my/id/eprint/39116 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |