Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network

Abstract

Patent categorization, which is to assign multiple International Patent Classification (IPC) codes to a patent document, relies heavily on expert efforts, as it requires substantial domain knowledge. When formulated as a multi-label text classification (MTC) problem, it draws two challenges to existing models: one is to learn effective document representations from text content; the other is to model the cross-section behavior of label set. In this work, we propose a label attention model based on graph convolutional network. It jointly learns the document-word associations and word-word co-occurrences to generate rich semantic embeddings of documents. It employs a non-local attention mechanism to learn label representations in the same space of document representations for multi-label classification. On a large CIRCA patent database, we evaluate the performance of our model and as many as seven competitive baselines. We find that our model outperforms all those prior state of the art by a large margin and achieves high performance on P@k and nDCG@k.