JCDL 2013 Report
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Transcript of JCDL 2013 Report
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JCDL 2013 Report
Kazunari Sugiyama
WING meeting23rd August, 2013
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Outline of JCDL13• Venue
– Indianapolis, Indiana, USA
x Indianapolis
JW Marriott X
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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
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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”
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• K. Sugiyama and M.-Y. Kan: “Exploiting Potential Citation Papers in Scholarly Paper Recommendation”
Vannevar Bush Best Paper Award
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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”
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“Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling”
[Outline]• Automatic annotation of metadata
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Tag recommendation
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“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)
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“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)
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“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
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“Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling”
[Experimental Results]
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“Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling”
[Example of recommended tags]
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