Bin Ni

Xiaolei Lu, Bin Ni

The automatic classification is a process of automatically assigning text documents to predefined categories. An accurate automatic patent classifier is crucial to patent inventors and patent examiners in terms of intellectual property protection, patent management, and patent information retrieval. We present BERT-CNN, a hierarchical patent classifier based on pre-trained language model by training the national patent application documents collected from the State Information Center, China. The experimental results show that BERT-CNN achieves 84.3% accuracy, which is far better than the two compared baseline methods, Convolutional Neural Networks and Recurrent Neural Networks. We didn't apply our model to the third and fourth hierarchical level of the International Patent Classification - "subclass" and "group".The visualization of the Attention Mechanism shows that BERT-CNN obtains new state-of-the-art results in representing vocabularies and semantics. This article demonstrates the practicality and effectiveness of BERT-CNN in the field of automatic patent classification.

Xiaolei Lu, Bin Ni

This article focuses on the study of Word Embedding, a feature-learning technique in Natural Language Processing that maps words or phrases to low-dimensional vectors. Beginning with the linguistic theories concerning contextual similarities - "Distributional Hypothesis" and "Context of Situation", this article introduces two ways of numerical representation of text: One-hot and Distributed Representation. In addition, this article presents statistical-based Language Models(such as Co-occurrence Matrix and Singular Value Decomposition) as well as Neural Network Language Models (NNLM, such as Continuous Bag-of-Words and Skip-Gram). This article also analyzes how Word Embedding can be applied to the study of word-sense disambiguation and diachronic linguistics.