A survey of cross-domain text categorization techniques

Murty, M. Ramakrishna; Murthy, J. V. R.; Reddy, Pvgd Prasad; Satapathy, Suresh Chandra

doi:10.1109/rait.2012.6194629

Cited by 18 publications

(2 citation statements)

References 3 publications

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“…In addition, OpenAIRE [30] Cross-domain IE motivate to create relationships between entities across numerous domains with a high level of coverage, unless the structures are similar but the roles are different. Usage of external data sources such as DBpedia, which extracts information from Wikipedia is integrated in scholarly domain to create extended relationships and support cross-domain text classification tasks [39].…”

Section: Common Scholarly Communication Infrastructuresmentioning

confidence: 99%

Scholarly knowledge graphs through structuring scholarly communication: a review

Verma

Bhatia

Harit

et al. 2022

Complex Intell. Syst.

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The necessity for scholarly knowledge mining and management has grown significantly as academic literature and its linkages to authors produce enormously. Information extraction, ontology matching, and accessing academic components with relations have become more critical than ever. Therefore, with the advancement of scientific literature, scholarly knowledge graphs have become critical to various applications where semantics can impart meanings to concepts. The objective of study is to report a literature review regarding knowledge graph construction, refinement and utilization in scholarly domain. Based on scholarly literature, the study presents a complete assessment of current state-of-the-art techniques. We presented an analytical methodology to investigate the existing status of scholarly knowledge graphs (SKG) by structuring scholarly communication. This review paper investigates the field of applying machine learning, rule-based learning, and natural language processing tools and approaches to construct SKG. It further presents the review of knowledge graph utilization and refinement to provide a view of current research efforts. In addition, we offer existing applications and challenges across the board in construction, refinement and utilization collectively. This research will help to identify frontier trends of SKG which will motivate future researchers to carry forward their work.

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Section: Common Scholarly Communication Infrastructuresmentioning

confidence: 99%

Scholarly knowledge graphs through structuring scholarly communication: a review

Verma

Bhatia

Harit

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

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“…The article [10] shows a review of cross-domain text categorization problem. Unlike the classical case, the training and the test data originates from different distributions or domains.…”

Section: Previous Workmentioning

confidence: 99%

Semantic classifier approach to document classification

Borkowski¹,

Ciesielski²,

Kłopotek³

2017

Preprint

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In this paper we propose a new document classification method, bridging discrepancies (so-called semantic gap) between the training set and the application sets of textual data. We demonstrate its superiority over classical text classification approaches, including traditional classifier ensembles. The method consists in combining a document categorization technique with a single classifier or a classifier ensemble (SemCom algorithm -Committee with Semantic Categorizer).

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