2002), for example, assume that if queries con-
taining one term often result in the selection of
(Cui et al., 2005) describe a fuzzy depen-
dency relation matching approach to passage re-
documents containing another term, then a strong
relationship between the two terms exist. In their
trieval in QA. Here, the authors present a statis-
approach, query terms and document terms are
tical technique to measure the degree of overlap
linked via sessions in which users click on doc-
between dependency relations in candidate sen-
uments that are presented as results for the query.
tences with their corresponding relations in the
(Riezler and Liu, 2010) apply a Statistical Ma-
question. Question/answer passage pairs from
TREC-8 and TREC-9 evaluations are used as
chine Translation model to parallel data consist-
ing of user queries and snippets from clicked web
training data. As in some of the papers mentioned
documents and in such a way extract contextual
earlier, a statistical translation model is used, but
expansion terms from the query rewrites.
this time to learn relatedness between paths. (Cui
We see our work as addressing the same fun-
et al., 2004) apply the same idea to answer ex-
traction. In each sentences returned by the IR
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