  Semi-supervised bootstrapping techniques for relationship extraction from
text iteratively expand a set of initial seed instances. Due to the lack of
labeled data, a key challenge in bootstrapping is semantic drift: if a false
positive instance is added during an iteration, then all following iterations
are contaminated. We introduce BREX, a new bootstrapping method that protects
against such contamination by highly effective confidence assessment. This is
achieved by using entity and template seeds jointly (as opposed to just one as
in previous work), by expanding entities and templates in parallel and in a
mutually constraining fashion in each iteration and by introducing
higherquality similarity measures for templates. Experimental results show that
BREX achieves an F1 that is 0.13 (0.87 vs. 0.74) better than the state of the
art for four relationships.
