Patent Classification Method Based on Depth Learning
摘要
The existing concept graph matching methods have the problem of poor matching fault tolerance.Therefore,this paper proposes a new method to explore the relationship between patent and effect.Long Short-term Memory(LSTM) is used to combine with bidirectional LSTM which based on the attention mechanism to train the patent corpus.The Softmax classification model is used to classify the patent and the effect of the patent is obtained.Experimental results show that the method uses the Bi-LSTM-ATT model to train has certain validity in determining the effect of patents,and the accuracy can reach more than 70%.
引用本文（GB/T 7714）
MA Jianhong,WANG Ruiyang,YAO Shuang,LIU Shuangyao. Patent Classification Method Based on Depth Learning[J]. DOAJ (DOAJ: Directory of Open Access Journals), 2018.
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