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A Spreading Activation Based Sentence Ordering Model Collaborated with Cognitive Logical Structure

机译:与认知逻辑结构协作的基于扩展激活的句子排序模型

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摘要

As novel web social Media emerges on the web, large-scale unordered sentences are springing up. Although these massive unordered sentences have rich information, loose semantic association and unordered distribution make users hard to order these sentences toward well semantic coherence manually. Automatic sentence ordering to maximize semantic coherence is a significantly practical problem. Some existing approaches have proposed some coherence measure methods of sentences, however some challenging issues have not been solved, which includes: 1) how to collaborate some basic cognitive logical structures into sentence ordering, 2) what is sound cognitive mechanism to guide sentence ordering. To solve these issues, we propose a spreading activation based sentences ordering model collaborated with cognitive logical structure. Our sentence ordering model collaborates with three basic cognitive logical structures, which includes summary is prior, illustrate follows, illustration is prior, summary follows and illustration is one by one. Besides, our sentence ordering model pursues coherence sentence order under guide by spreading activation, which actives knowledge from most associated keywords to most associated sentences. To validate correctness of our model, some experiments are conducted to measure the accuracy of the automatically generated sentence order. The results show our model can give sentences order with higher accuracy in less iteration. The sentence ordering model can be applied in automatic text organization and summarization.
机译:随着新颖的网络社交媒体出现在网络上,大量的无序句子如雨后春笋般冒出来。尽管这些庞大的无序句子具有丰富的信息,但是松散的语义关联和无序的分布使用户很难手动将这些句子排序为语义一致的语言。自动句子排序以最大化语义连贯性是一个重大的实际问题。现有的一些方法已经提出了一些句子的连贯性度量方法,但是还没有解决一些具有挑战性的问题,其中包括:1)如何将一些基本的认知逻辑结构协作到句子的排序中; 2)什么是指导句子排序的合理的认知机制。为了解决这些问题,我们提出了与认知逻辑结构协作的基于扩展激活的句子排序模型。我们的句子排序模型与三个基本的认知逻辑结构协作,其中包括摘要是先验的,说明是跟随的,插图是先验的,摘要是跟随的,插图是一个接一个的。此外,我们的句子排序模型通过扩展激活,在引导下追求连贯的句子顺序,从而将知识从最相关的关键字激活到最相关的句子。为了验证我们模型的正确性,进行了一些实验来测量自动生成的句子顺序的准确性。结果表明,我们的模型可以在更少的迭代中以更高的准确度给出句子顺序。句子排序模型可以应用于自动文本组织和摘要中。

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