دورية أكاديمية
Contextual topic discovery using unsupervised keyphrase extraction and hierarchical semantic graph model
| العنوان: | Contextual topic discovery using unsupervised keyphrase extraction and hierarchical semantic graph model |
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| المؤلفون: | Hung Du, Srikanth Thudumu, Antonio Giardina, Rajesh Vasa, Kon Mouzakis, Li Jiang, John Chisholm, Sanat Bista |
| المصدر: | Journal of Big Data, Vol 10, Iss 1, Pp 1-19 (2023) |
| بيانات النشر: | SpringerOpen, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Information technology LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Context-awareness, Contextual topic discovery, Hierarchical semantic graph, Keyphrase extraction, Topic modeling, Computer engineering. Computer hardware, TK7885-7895, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Abstract Recent technological advancements have led to a significant increase in digital documents. A document’s key information is generally represented by the keyphrases that provide the abstract description contained therein. With traditional keyphrase techniques, however, it is difficult to identify relevant information based on context. Several studies in the literature have explored graph-based unsupervised keyphrase extraction techniques for automatic keyphrase extraction. However, there is only limited existing work that embeds contextual information for keyphrase extraction. To understand keyphrases, it is essential to grasp both the concept and the context of the document. Hence, a hybrid unsupervised keyphrase extraction technique is presented in this paper called ContextualRank, which embeds contextual information such as sentences and paragraphs that are relevant to keyphrases in the keyphrase extraction process. We propose a hierarchical topic modeling approach for topic discovery based on aggregating the extracted keyphrases from ContextualRank. Based on the evaluation on two short-text datasets and one long-text dataset, ContextualRank obtains remarkable improvements in performance over other baselines in the short-text datasets. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2196-1115 |
| Relation: | https://doaj.org/toc/2196-1115 |
| DOI: | 10.1186/s40537-023-00833-1 |
| URL الوصول: | https://doaj.org/article/f3f51ce7d01d4a2a91856c28a041550e |
| رقم الأكسشن: | edsdoj.f3f51ce7d01d4a2a91856c28a041550e |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 21961115 |
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| DOI: | 10.1186/s40537-023-00833-1 |