相关性(法律)
计算机科学
人工智能
班级(哲学)
深度学习
深层神经网络
图层(电子)
机器学习
航程(航空)
数据科学
工程类
化学
有机化学
政治学
法学
航空航天工程
作者
Md Shajalal,Sebastian Denef,Md. Rezaul Karim,Alexander Boden,Gunnar Stevens
出处
期刊:Communications in computer and information science
日期:2023-01-01
卷期号:: 457-474
标识
DOI:10.1007/978-3-031-44067-0_24
摘要
Recent technological advancements have led to a large number of patents in a diverse range of domains, making it challenging for human experts to analyze and manage. State-of-the-art methods for multi-label patent classification rely on deep neural networks (DNNs), which are complex and often considered black-boxes due to their opaque decision-making processes. In this paper, we propose a novel deep explainable patent classification framework by introducing layer-wise relevance propagation (LRP) to provide human-understandable explanations for predictions. We train several DNN models, including Bi-LSTM, CNN, and CNN-BiLSTM, and propagate the predictions backward from the output layer up to the input layer of the model to identify the relevance of words for individual predictions. Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class. Experimental results on two datasets comprising two-million patent texts demonstrate high performance in terms of various evaluation measures. The explanations generated for each prediction highlight important relevant words that align with the predicted class, making the prediction more understandable. Explainable systems have the potential to facilitate the adoption of complex AI-enabled methods for patent classification in real-world applications.
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