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DUTH does Probabilities of Relevance at the Legal Track Dim P. Papadopoulos Vicky S. Kalogeiton Avi Arampatzis
 

Summary: DUTH does Probabilities of Relevance at the Legal Track
Dim P. Papadopoulos Vicky S. Kalogeiton Avi Arampatzis
Department of Electrical and Computer Engineering,
Democritus University of Thrace,
Xanthi 67100, Greece.
{dimipapa4,vasikalo,avi}@ee.duth.gr
Abstract
We participated in the Learning Task of the TREC 2010
Legal Track, focusing solely on estimating probabilities
of relevance. We submitted three automated runs based
on the same tf.idf ranking, produced by the topic narra-
tives and positive-only feedback of the training data in
equal contributions. The runs differ in the way the prob-
abilities of relevance are estimated: (1) DUTHsdtA em-
ployed the Truncated Normal-Exponential model to turn
scores to probabilities. (2) DUTHsdeA did not assume
any specific component score distributions but estimated
those on the scores of training data via Kernel Density
Estimation (KDE) methods. (3) DUTHlrgA used Lo-
gistic Regression with the co-efficients estimated on the

  

Source: Arampatzis, Avi - Department of Electrical and Computer Engineering, Democritus University of Thrace

 

Collections: Computer Technologies and Information Sciences