A hybrid strategy to extract metadata from scholarly articles by utilizing support vector machine and heuristics.

Autor/inn/en	Waqas, Muhammad; Anjum, Nadeem; Afzal, Muhammad Tanvir
Titel	A hybrid strategy to extract metadata from scholarly articles by utilizing support vector machine and heuristics.
Quelle	In: Scientometrics, (2023) 8, S.4349-4382 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-023-04774-7
Schlagwörter	Features engineering; Machine learning; Research article; Metadata extraction; Textmining
Abstract	Abstract The immense growth in online research publications has attracted the research community to extract valuable information from scientific resources by exploring online digital libraries and publishers’ websites. The metadata stored in a machine comprehendible form can facilitate a precise search to enlist most related articles by applying semantic queries to the document’s metadata and the structural elements. The online search engines and digital libraries offer only keyword-based search on full-body text, which creates excessive results. The research community in recent years has adopted different approaches to extract structural information from research documents. We have distributed the content of an article into two logical layouts and metadata levels. This strategy has given our technique an advantage over the state-of-the-art (SOTA) extracting metadata with diversified publication styles. The experimental results have revealed that the proposed approach has shown a significant gain in performance of 20.26% to 27.14%.
Erfasst von	OLC
Update	2024/1/01