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Overview of JCDL 2013: Review Process, Acceptance Rates, and Notable Paper Awards

The JCDL 2013 conference, held on August 23, 2013, at JW Marriott, Indianapolis, focused on the review process involving three reviewers per submission and two additional meta-reviewers for comprehensive evaluation. The acceptance rate was 29.9%, with emphasis on full and short papers. Future JCDL conferences are planned in London (2014) and possibly Tennessee/New York (2015-2016). Notable paper nominees include groundbreaking research on document clustering, mobile access for digital libraries, and user comment identification in social media.

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Overview of JCDL 2013: Review Process, Acceptance Rates, and Notable Paper Awards

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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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