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A Generic approach for Pronominal Anaphora and Zero Anaphora resolution in Arabic language

机译:阿拉伯语中双相神经神经神经神经统治者分辨率的通用方法

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This paper deals with the resolution of pronominal anaphora and zero anaphora in Arabic language. While researchers have treated the two phenomena separately, we propose a generic approach for both of them. Our resolution system combines a Q-learning reinforcement method and Word Embedding models. The Q-learning method uses syntactic criteria as preference factors to select candidate antecedents. It reinforces the best combination criteria for evaluating candidate antecedents. The Word Embedding models provide semantic similarity measures that help to validate the best antecedent. Our approach is evaluated on different type of Arabic texts and the obtained precision can reach79.37%.
机译:本文涉及阿拉伯语中的分辨性神经神经治疗和零阿帕拉。虽然研究人员分别对待了这两种现象,但我们向其中两者提出了一种通用方法。我们的分辨率系统结合了Q学习强化方法和Word嵌入模型。 Q-Learning方法使用句法标准作为选择候选前一种的偏好因素。它强化了评估候选人前一种的最佳组合标准。嵌入模型的单词提供了语义相似度措施,有助于验证最佳的前进状态。我们的方法是在不同类型的阿拉伯语文本上进行评估,获得的精度可以达到79.37%。

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