papers AI Learner
The Github is limit! Click to go to the new site.

Un duel probabiliste pour d'epartager deux pr'esidents

2019-03-11
Marc El-Bèze, Juan-Manuel Torres-Moreno, Frédéric Béchet

Abstract

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.

Abstract (translated by Google)
URL

http://arxiv.org/abs/1903.07397

PDF

http://arxiv.org/pdf/1903.07397


Similar Posts

Comments