  We present a set of probabilistic models applied to binary classification as
defined in the DEFT'05 challenge. The challenge consisted a mixture of two
differents problems in Natural Language Processing : identification of author
(a sequence of Fran\c{c}ois Mitterrand's sentences might have been inserted
into a speech of Jacques Chirac) and thematic break detection (the subjects
addressed by the two authors are supposed to be different). Markov chains,
Bayes models and an adaptative process have been used to identify the paternity
of these sequences. A probabilistic model of the internal coherence of speeches
which has been employed to identify thematic breaks. Adding this model has
shown to improve the quality results. A comparison with different approaches
demostrates the superiority of a strategy that combines learning, coherence and
adaptation. Applied to the DEFT'05 data test the results in terms of precision
(0.890), recall (0.955) and Fscore (0.925) measure are very promising.
