Abstract
Plagiarism is usually studied from an analysis viewpoint: how to detect that a text contains copies of another one. In this chapter we study plagiarism from the generation viewpoint: how to generate a text with a guarantee of non-plagiarism. More precisely, we address the problem of Markov sequence generation with forbidden k-gram constraints. This problem is addressed in two steps. In the first step, we show that, given a Markov transition matrix and a set of k-grams, we can build efficiently an automaton that represents exactly the language of all sequences that can be generated from a Markov model, and that also do not contain any of the k-grams. The size of the automaton is bounded by the size of the forbidden k-grams, and so is the time for building it. This automaton can be used to solve the algebraic problem (i.e. considering non-zero probabilities are uniform), by a simple walk. In the second step, we show that the automaton can be extended so as to be exploited by a belief propagation scheme, in order to produce perfect sampling of all the solutions.
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Papadopoulos, A., Pachet, F., Roy, P. (2016). Generating Non-plagiaristic Markov Sequences with Max Order Sampling. In: Degli Esposti, M., Altmann, E., Pachet, F. (eds) Creativity and Universality in Language. Lecture Notes in Morphogenesis. Springer, Cham. https://doi.org/10.1007/978-3-319-24403-7_6
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DOI: https://doi.org/10.1007/978-3-319-24403-7_6
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