基于双通道LSTM的用户年龄识别方法
发布时间：2018-07-13 13:58
【摘要】：传统的年龄回归方法不能学习深层次信息,因此利用能充分挖掘上下文关系信息的深度学习方法来识别用户的年龄。具体而言,提出了一种基于LSTM的年龄回归方法,其能够学习长期依赖关系即建立输入值之间的长相关联系。采用了两种不同的特征,即文本特征和社交特征。为了有效地区分这两种特征,充分利用这两种特征之间的信息,进一步提出了基于双通道LSTM的年龄回归方法,具体实现是在神经网络中加入Merge层,将LSTM分别产生的文本特征表示和社交特征表示结合进行集成学习以充分学习文本特征和社交特征间的联系。实验结果表明,基于双通道LSTM的年龄回归方法能够有效地区分文本特征和社交特征,并且较单个LSTM方法能够取得更好的年龄回归性能。
[Abstract]:The traditional age regression method can not learn the deep level information, so the depth learning method which can fully mine the contextual information can be used to identify the age of the user. Specifically, an age regression method based on LSTM is proposed, which can learn long-term dependency, that is, establish long correlation between input values. Two different features, text feature and social feature, are adopted. In order to effectively distinguish the two features and make full use of the information between the two features, a new age regression method based on two-channel LSTM is proposed, which is realized by adding merge layer into neural network. The text feature representation and the social feature representation generated by LSTM are integrated to learn the relationship between the text feature and the social feature. The experimental results show that the age regression method based on two-channel LSTM can effectively distinguish text features from social features, and can achieve better age regression performance than the single LSTM method.
【作者单位】： 苏州大学自然语言处理实验室;
【基金】：国家自然科学基金重点资助项目(61331011);国家自然科学基金资助项目(61375073,61273320)
【分类号】：O212.1;TP391.1
，
本文编号：2119604
[Abstract]:The traditional age regression method can not learn the deep level information, so the depth learning method which can fully mine the contextual information can be used to identify the age of the user. Specifically, an age regression method based on LSTM is proposed, which can learn long-term dependency, that is, establish long correlation between input values. Two different features, text feature and social feature, are adopted. In order to effectively distinguish the two features and make full use of the information between the two features, a new age regression method based on two-channel LSTM is proposed, which is realized by adding merge layer into neural network. The text feature representation and the social feature representation generated by LSTM are integrated to learn the relationship between the text feature and the social feature. The experimental results show that the age regression method based on two-channel LSTM can effectively distinguish text features from social features, and can achieve better age regression performance than the single LSTM method.
【作者单位】： 苏州大学自然语言处理实验室;
【基金】：国家自然科学基金重点资助项目(61331011);国家自然科学基金资助项目(61375073,61273320)
【分类号】：O212.1;TP391.1
，
本文编号：2119604
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