Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification
This work addresses the challenge of making AI methods more interpretable for patent classification, which is incremental as it applies existing explainability techniques to a specific domain.
The paper tackles the problem of opaque decision-making in deep neural networks for multi-label patent classification by introducing a novel deep explainable framework using layer-wise relevance propagation (LRP) to generate human-understandable explanations, achieving high performance on two datasets with two-million patent texts.
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.