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JCDL 2013 Report

JCDL 2013 Report. Kazunari Sugiyama WING meeting 23 rd August, 2013. Outline of JCDL13. Venue Indianapolis, Indiana, USA. X. JW Marriott. x Indianapolis. Outline of JCDL13. Review Process For each submission, (1) 3 reviewers read and rate for each paper,

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JCDL 2013 Report

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  1. JCDL 2013 Report Kazunari Sugiyama WING meeting 23rd August, 2013

  2. Outline of JCDL13 • Venue • Indianapolis, Indiana, USA X JW Marriott x Indianapolis

  3. Outline of JCDL13 • Review Process • For each submission, (1) 3 reviewers read and rate for each paper, (2) Then each paper was read by 2 additional meta-reviewers • Acceptance rate • 29.9% [50 / 167] • Full paper : 28 / 95 (29.4%) • Short paper: 22 / 72 (30.6%) • Future JCDL • 2014: London, UK • 8-12 Sep., Joint with TPDL (Theory and Practice of Digital Libraries) • 2015, 2016: Tennessee or New York • 2017: European country

  4. Nominees for Best Papers • W. Ke: “Information-theoretic Term Weighting Schemes for Document Clustering” • A. Hinze and D. Bainbridge: “Tipple: Location-Triggered Mobile Access to a Digital Library for Audio Books” • P. Bogen, A. McKenzie, and R. Gillen: “Redeye: A Digital Library for Forensic Document Triage” • K. Sugiyama and M.-Y. Kan: “Exploiting Potential Citation Papers in Scholarly Paper Recommendation” Vannevar Bush Best Paper Award

  5. Nominees for Best Student Papers • E. Momeni, K. Tao, B. Haslhofer, and G.-J. Houben: “Identification of Useful User Comments in Social Media: A Case Study on Flickr Commons” • S. Ainsworth and M. Nelson: “Evaluating Sliding and Sticky Target Policies by Measuring Temporal Drift in Acyclic Walks Through a Web Archive” • S. D. Torres, D.Hiemstra, and T. Huibers: “Vertical Selection in the Information Domain of Children” • S. Tuarob and L. C. Pouchard, and C. Lee Giles: “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling”

  6. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Outline] • Automatic annotation of metadata Tag recommendation

  7. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Approach] • TF-IDF • Topic model • Baseline: • I. H. Witten et al.: “KEA: Practical Automatic Keyphrase Extraction (DL’99)

  8. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Experimental Data] • The Oak Ridge National Laboratory Distributed Active Archive Center (DAAC) • Dryad Digital Repository (DRYAD) • The Knowledge Network for Biocomplexity (KNB) • TreeBASE: A Repository of Phylogenetic Information (TreeBASE)

  9. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Evaluation Measures] • Precision, Recall, F1 • Mean Reciprocal Rank (MRR) • Binary Preference (Bpref) • A measure that can take the order of recommended tags into account

  10. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Experimental Results]

  11. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Example of recommended tags] 7/20 15/20 2/20

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