A Two Phase Deep Learning Model for Identifying Discrimination from Tweets
A Two Phase Deep Learning Model for Identifying Discrimination from Tweets
复制标题
用于识别推文歧视的两阶段深度学习模型
DOI：
10.5441/002/edbt.2016.92
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Yang Xiang
中科院分区：
文献类型：
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作者：
Shuhan Yuan;Xintao Wu;Yang Xiang
歧视发现是通过分析历史决策记录的数据集来揭示歧视性做法的数据挖掘问题。在本文中，我们专注于使用深度学习模型从推文中发现歧视。这里的一个挑战是，为了进行区分分析，需要获得训练深度学习模型所需的大型标记良好的数据集。我们开发了一个两阶段深度学习模型来应对这一挑战。我们的模型rst基于弱标记的tweet（包含一些特殊的hashtag）学习文本表示，然后训练分类器
Discrimination discovery is the data mining problem of unveiling discriminatory practices by analyzing a dataset of historical decision records. In this paper, we focus on discovering discrimination from tweets using deep learning models. One challenge here is that it is dicult to obtain a large well-labeled dataset required by the training of deep learning models for the purpose of discrimination analysis. We develop a two-phase deep learning model to address this challenge. Our model rst learns text representations based on weakly-labeled tweets (containing some specic hashtags), then trains the classier