ABSTRACT
Social research networks such as Mendeley and CiteULike offer various services for collaboratively managing bibliographic metadata and uploading textual artifacts. One core problem thereby is the extraction of bibliographic metadata from the textual artifacts. Our work investiages the use of Conditional Random Fields and Support Vector Machines, implemented in two state-of-the-art real-world systems, namely ParsCit and the Mendeley Desktop, for automatically extracting bibliographic metadata. We compare the systems' accuracy on two newly created real-world data sets gathered from Mendeley and Linked-Open-Data repositories. Our analysis shows that two-stage SVMs provide reasonable performance in solving the challenge of metadata extraction from user-provided textual artifacts.
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- H. Han, E. Manavoglu, H. Zha, K. Tsioutsiouliklis, C. L. Giles, and X. Zhang. Rule-based word clustering for document metadata extraction. In Proceedings of the 2005 ACM symposium on Applied computing - SAC '05, page 1049, New York, New York, USA, 2005. ACM Press. Google ScholarDigital Library
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Index Terms
- A comparison of metadata extraction techniques for crowdsourced bibliographic metadata management
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