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https://clgiles.ist.psu.edu/papers/JCDL-2003-automata-metdata.pdf
Automatic Document Metadata Extraction using Support Vector Machines Hui Han C. Lee Giles Eren Manavoglu Hongyuan Zha Department of Computer Science and Engineering The School of Information Sciences and Technology The Pennsylvania State UniversityUniversity Park, PA, 16802 hhan,zha,manavogl @cse.psu.edu [email protected] Zhenyue Zhang
https://ieeexplore.ieee.org/document/1204842/
Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and ada Automatic document metadata extraction using support vector machines - IEEE Conference Publication Skip to Main ContentCited by: 432
https://dl.acm.org/citation.cfm?id=827140.827146
Machine learning methods offer robust and adaptable automatic metadata extraction. We describe a Support Vector Machine classification-based method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on the same task.Cited by: 432
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.3718
Machine learning methods offer robust and adaptable automatic metadata extraction. We describe a Support Vector Machine classification-based method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on the same task.
https://www.academia.edu/10323147/Automatic_document_metadata_extraction_using_support_vector_machines
Automatic document metadata extraction using support vector machines
https://www.emerald.com/insight/content/doi/10.1108/00330331111182094/full/html
Sep 27, 2011 · The proposed system for automatic metadata extraction using support vector machines model was integrated into the software system, CRIS UNS. Metadata extraction has been tested on the publications of researchers from the Department of Mathematics and Informatics of the Faculty of Sciences in Novi Sad.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3004227/
A combination of Support Vector Machine and Hidden Markov Model is used to create the layout recognition models from a training set of the corpus, following which a rule-based metadata search model is used to extract the embedded metadata by analyzing the string patterns within and surrounding each field in the recognized layouts.Cited by: 6
https://core.ac.uk/display/21083395
Machine learning methods offer robust and adaptable automatic metadata extraction. We describe a Support Vector Machine classification-based method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on the same task.
https://www.researchgate.net/publication/220195713_Automated_document_metadata_extraction
Practical implications – The proposed system for automatic metadata extraction using support vector machines model was integrated into the software system, CRIS UNS.
http://www.jetir.org/papers/JETIR1407014.pdf
(1)Automatic Document Metadata Extraction using Support Vector Machines This discusses a machine learning method for automatic metadata extraction. They extend Meta tags from document and mapping it with the Dublin Core 15 Metadata Element Extend the SVM into multi-class classifiers in the “One class versus all others”
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