2.1 Statistical Translation Models for
have used parallel data extracted from the retrieval
Retrieval
corpus itself. The translation models obtained are
Statistical translation models for retrieval have
therefore domain and collection-specific, which
first been introduced by Berger and Lafferty
introduces a bias in the evaluation and makes
(1999). These models attempt to address syn-
it difficult to assess to what extent the transla-
onymy and polysemy problems by encoding sta-
tion model may be re-used for other tasks and
tistical word associations trained on monolingual
document collections. We henceforth propose a
parallel corpora. This method offers several ad-
new approach for building monolingual transla-
vantages. First, it bases upon a sound mathe-
tion models relying on domain-independent lexi-
matical formulation of the retrieval model. Sec-
cal semantic resources. Moreover, we extensively
ond, it is not as computationally expensive as
compare the results obtained by these models with
other semantic retrieval models, since it only re-
models obtained from a different type of dataset,
lies on a word translation table which can easily
namely Question & Answer archives.
be computed before retrieval. The main draw-
back lies in the availability of suitable training data
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