  Spoken Language Systems at Saarland University (LSV) participated this year
with 5 runs at the TAC KBP English slot filling track. Effective algorithms for
all parts of the pipeline, from document retrieval to relation prediction and
response post-processing, are bundled in a modular end-to-end relation
extraction system called RelationFactory. The main run solely focuses on
shallow techniques and achieved significant improvements over LSV's last year's
system, while using the same training data and patterns. Improvements mainly
have been obtained by a feature representation focusing on surface skip n-grams
and improved scoring for extracted distant supervision patterns. Important
factors for effective extraction are the training and tuning scheme for distant
supervision classifiers, and the query expansion by a translation model based
on Wikipedia links. In the TAC KBP 2013 English Slotfilling evaluation, the
submitted main run of the LSV RelationFactory system achieved the top-ranked
F1-score of 37.3%.
