Deep Word Association:A Flexible Chinese Word Association Method with Iterative Attention Mechanism
Word association is to predict the subsequent words and phrase,acting as a reminder to accelerate the text-editing process.Existing word association models can only predict the next word inflexibly through a given word vocabulary or a simply back-off N-gram language model.Herein,we propose a deep word association system based on attention mechanism with the following contributions:(1)To the best of our knowledge,this is the first investigation of an attention-based recurrent neural network for word association.In the experiments,we provide a comprehensive study on the attention processes for the word association problem;(2)An novel approach,named DropContext,is proposed to solve the over-fitting problem during attention training procedure;(3)Compared with conventional vocabulary-based methods,our word association system can generate an arbitrary-length string of words that are reasonable;(4)Given information on different hierarchies,the proposed system can flexibly generate associated words accordingly.
Word association Attention mechanism Recurrent neural network Chinese DropContext
Yaoxiong Huang Zecheng Xie Manfei Liu Shuaitao Zhang Lianwen Jin
School of Electronic and Information Engineering,South China University of Technology,Guangzhou,Chin School of Electronic and Information Engineering,South China University of Technology,Guangzhou,Chin
国际会议
广州
英文
112-123
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）