Multi-label Ranking with LSTM2 for Document Classification
Multi-label document classification is an important challenge with many real-world applications.While multi-label ranking is a common approach for multi-label classification.However existing works usually suffer from incomplete and context-free representation,and nonautomatic and part based model implementation.To solve the problem,we propose a LSTM2(Long short term memory)model for document classification in this paper.This model consists of two-steps.The first is repLSTM process which is based on supervised LSTM by introducing the document labels to learn document representation.The second is rankLSTM process.The order of documents labels are rearranged in accordance with a semantics tree,which better exerts the advantages of the LSTM in sequence.Besides by predicting label serially,the model can be trained as a whole.In addition,Connectionist Temporal Classification is used in this process which is a good solution to deal with the error propagation for variable length output(the number of labels in each document).Experiments on three generalization datasets have achieved good results.
Document classification Multi-label ranking repLSTM rankLSTM
Yan Yan Xu-Cheng Yin Chun Yang Bo-Wen Zhang Hong-Wei Hao
School of Mechanical Electronic and Information Engineering,China University of Mining and Technolog University of Science and Technology Beijing,Beijing,China Institute of Automation,Chinese Academy of Sciences,Beijing,China
国际会议
第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)
成都
英文
349-363
2016-11-03（万方平台首次上网日期，不代表论文的发表时间）