基于重构的领域自适应灾害推文分类

Xukun Li, Doina Caragea
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引用次数: 11

摘要

在灾难开始时实时识别关键信息是一项具有挑战性但又很重要的任务。这个任务最近已经通过使用领域适应方法来解决,该方法消除了对目标标记数据的需要,从而可以加速识别有用信息的过程。我们建议研究领域重建分类网络(DRCN)方法在灾难推文上的有效性。DRCN通过自编码器重构目标数据来适应信息。使用序列到序列自编码器的实验结果表明,DRCN方法可以提高监督基线模型和领域自适应基线模型的性能。
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Domain Adaptation with Reconstruction for Disaster Tweet Classification
Identifying critical information in real time in the beginning of a disaster is a challenging but important task. This task has been recently addressed using domain adaptation approaches, which eliminate the need for target labeled data, and can thus accelerate the process of identifying useful information. We propose to investigate the effectiveness of the Domain Reconstruction Classification Network (DRCN) approach on disaster tweets. DRCN adapts information from target data by reconstructing it with an autoencoder. Experimental results using a sequence-to-sequence autoencodershow that the DRCN approach can improve the performance of both supervised and domain adaptation baseline models.
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