This  thesis  presents  diverse  contributions  in the  fields of text
classification, machine translation  and computer-assisted translation
under the statistical framework.

In  text  classification,  a  new  application  called  bilingual text
classification is proposed together with a series of models to capture
bilingual  information. To  this  purpose  two  main  approaches  were
presented,    the   first    of   them   is    based   on    a   naive
crosslingual-independent   assumption   and   the  second,   on a more
sophisticated  crosslingual  word-correlation framework. As far as the
naive assumption is concerned, five unigram models and smoothed n-gram
languages models are introduced.  These models were evaluated on three
tasks of increasing complexity,  considering the most complex of these
tasks  under the  viewpoint  of  a  bilingual  machine-aided  indexing
application.   The   crosslingual   word-correlation    framework   is
represented by bilingual models that integrate a translation model. In
our  case  this  model  is  the well-known  M1  translation  model  in
conjunction with a  unigram model. This model was tested on two of the
simpler    previously    mentioned   tasks   superseding   the   naive
approximation.

In machine  translation,  the statistical  word-alignment  translation
models  M1,  M2  and  HMM are  extended under  the  mixture  modelling
approach in order to  define context-specific  translation models.  In
the case of the M2 model,  a mixture extension of  an already existing
iterative  dynamic-programming  search algorithm  for the  M2 model is
also defined.  This search algorith  allows us to directly  assess the
translation  quality of  the M2  mixture  model  on a  semi-artificial
controlled task, obtaining statistically significant improvements over
the  conventional  M2  model.  Moreover,  an   extensive  experimental
evaluation  of these three  models is  carried out  on two  well-known
shared tasks.  These two tasks are used to assess on the one hand, the
quality of the  alignments obtained as  a byproduct of  the M1, M2 and
HMM  models  and  on the  other  hand, the  translation  quality  of a
statistical  phrase-based system  seeded with these  alignments.  As a
result  of  this  evaluation  we  observed that  the M2  mixture model
offered statistically significant betterment in alignment quality with
respect to the conventional M2 model.  In addition, the  evaluation of
translation   quality  brought  to   light   slight,   but  systematic
improvements  in  translation  quality for all three models, achieving
state-of-the-art results for the HMM mixture model.

Finally, an interactive  and predictive  computer-assisted translation
system based on stochastic finite-state transducers is presented. This
system  integrates  well-known efficient  error-correcting  and n-best
parsing  algorithms  that  are  adapted  and  implemented  in order to
guarantee low response time,  while  preserving  adequate  translation
quality. The system was automatically tested on two corpora devoted to
technical user manuals and bulletins of the European Union. The former
corpus  served  as  a  bedtest  for  a  thoroughly  manual  evaluation
performed by translation  agencies  involved in  the European  project
TransType2.   Both,  automatic  and  manual  evaluations   reported  a
significant  reduction i n typing effort, speeding up the  translation
process,   and   achieving so,  the  final  goal  of computer-assisted
translation systems.