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Classification of Short-Texts Generated During Disasters: A Deep Neural Network Based Approach

机译:灾难期间生成的短文本分类:一种基于深度神经网络的方法

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摘要

Micro-blogging sites provide a wealth of resources during disaster events in the form of short texts. Correct classification of these text data into various actionable classes can be of great help in shaping the means to rescue people in disaster-affected places. The process of classification of these text data poses a challenging problem because the texts are usually short and very noisy and finding good features that can distinguish these texts into different classes is time consuming, tedious and often requires a lot of domain knowledge. We propose a deep learning based model to classify tweets into different actionable classes such as resource need and availability, activities of various NGO etc. Our model requires no domain knowledge and can be used in any disaster scenario with little to no modification.
机译:微博站点在灾难事件期间以短文本的形式提供了丰富的资源。将这些文本数据正确分类为各种可操作的类别,对于塑造在受灾地区救助人员的手段很有帮助。这些文本数据的分类过程提出了一个具有挑战性的问题,因为这些文本通常简短且非常嘈杂,并且找到可以将这些文本区分为不同类别的良好功能非常耗时,乏味且通常需要大量领域知识。我们提出了一个基于深度学习的模型,将推文分为不同的可操作类别,例如资源需求和可用性,各种NGO的活动等。我们的模型不需要领域知识,并且几乎不需要修改就可以用于任何灾难情况。

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