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Anaphora Resolution by Antecedent Identification Followed by Anaphoricity Determination

机译:通过先验识别和后照度确定来实现后照度解析

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摘要

We propose a machine learning-based approach to noun-phrase anaphora resolution that combines the advantages of previous learning-based models while overcoming their drawbacks. Our anaphora resolution process reverses the order of the steps in the classification-then-search model proposed by Ng and Cardie [2002b], inheriting all the advantages of that model. We conducted experiments on resolving noun-phrase anaphora in Japanese. The results show that with the selection-then-classification-based modifications, our proposed model outperforms earlier learning-based approaches.
机译:我们提出了一种基于机器学习的名词短语回指解析方法,该方法结合了以前基于学习的模型的优点,同时克服了它们的缺点。我们的回指解析过程颠倒了Ng和Cardie [2002b]提出的“分类-然后-搜索”模型中步骤的顺序,继承了该模型的所有优点。我们进行了实验来解决日语中的名词短语反指法。结果表明,通过基于选择然后分类的修改,我们提出的模型优于早期的基于学习的方法。

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