Bidirectional Sequence Classification for Tagging Tasks with Guided Learning
نویسنده
چکیده
In this paper we present a series of adaptations of the Guided Learning framework to solve different tagging tasks. The specificity of the proposed system lies in its ability to learn the order of inference together with the parameters of the local classifier instead of forcing it into a pre-defined order (left-to-right). The training algorithm is based on the Perceptron Algorithm. We apply the system to different kinds of tagging tasks reaching state of the art results with short execution time. Mots-clés : Bidirectionnel, Classification de Séquence, Apprentissage Guidé.
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