JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (7): 91-96.doi: 10.6040/j.issn.1671-9352.1.2016.019

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User age regression with dual-channel LSTM

CHEN Jing, LI Shou-shan*, ZHOU Guo-dong   

  1. Natural Language Processing Lab, Soochow University, Suzhou 215006, Jiangsu, China
  • Received:2016-12-09 Online:2017-07-20 Published:2017-07-07

Abstract: Traditional age regression approach cant learn context relation, so we utilize deep learning approach which could make full use of context relation to predict users age. Specific implementation is that we propose a age regression approach based on LSTM. LSTM can learn long short-term memory, namely building long relevant connection between input values. We utilize two different features, namely textual and social features. In order to distinguish the two features and make full use of them, we propose a new age regression approach based on dual-channel LSTM. Specific implementation is to add a Merge layer into LSTM, combing text features representation and social features representation generated by LSTM, to fully learn knowledge between textual and social features. Experimental results show that our method can effectively distinguish textual and social features and improve the performance of age regression.

Key words: LSTM, textual features, age regression, social features

CLC Number: 

  • TP391
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