Abstract
The anaphora resolution belongs to the attractive problems of the NLP field. In this paper, we treat the problem of resolving pronominal anaphora which are very abundant in Arabic texts. Our approach includes a set of steps; namely: the identification of anaphoric pronouns, removing the non-referential ones, identification of the lists of candidates from the context surrounding the identified anaphora and choosing the best candidate for each anaphoric pronoun. The last two steps could be seen as a dynamic and probabilistic process that consists of a sequence of decisions and could be modeled using a Markov Decision Process (MDP). In addition, we have opted for a reinforcement learning approach because it is an effective method for learning in an uncertain and stochastic environment like ours. Also, it could resolve the MDPs. In order to evaluate the proposed approach, we have developed a system that gives us encouraging results. The resolution accuracy reaches up to 80%.
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Trabelsi, F.B.F., Ben Othmane Zribi, C., Mathlouthi, S. (2018). Arabic Anaphora Resolution Using Markov Decision Process. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_37
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DOI: https://doi.org/10.1007/978-3-319-75477-2_37
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