'University of Aden - Faculty of Economics and Administration'
Abstract
We participated in two tracks: Knowledge Base Acceleration (KBA)
Track and Session Track. In the KBA track, we focused on experi-
menting with different approaches as it is the first time the track is
launched. We experimented with supervised and unsupervised re-
trieval models. Our supervised approach models include language
models and a string-learning system. Our unsupervised approaches
include using: 1)DBpedia labels and 2) Google-Cross-Lingual Dic-
tionary (GCLD). While the approach that uses GCLD targets the
central and relvant bins, all the rest target the central bin. The
GCLD and the string-learning system have outperformed the oth-
ers in their respective targeted bins. The goal of the Session track
submission is to evaluate whether and how a logic framework for
representing user interactions with an IR system can be used for
improving the approximation of the relevant term distribution that
another system that is supposed to have access to the session infor-
mation will then calculate.
the documents in the stream corpora. Three out of the seven runs
used a Hadoop cluster provide by Sara.nl to process the stream cor-
pora. The other 4 runs used a federated access to the same corpora
distributed among 7 workstations