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A Use Case of patent classification Using Deep Learning with Transfer Learning

Journal of Data and Information Science 2022年第3期7卷 49-70页

作者： Roberto Henriques Adria Ferreira Mauro Castelli Campus de Campolide Lisboa 1070-312Portugal

Purpose:patent classification is one of the areas in Intellectual Property Analytics(IPA),and a growing use case since the number of patent applications has been increasing *** propose using machine learning algorithms to classify Portuguese patents and evaluate the performance of transfer learning methodologies to solve this ***/methodology/approach:We applied three different approaches in this ***,we used a dataset available by INPI to explore traditional machine learning algorithms and ensemble *** preprocessing data by applying TF-IDF,FastText and Doc2Vec,the models were evaluated by cross-validation in 5 *** a second approach,we used two different Neural Networks architectures,a Convolutional Neural Network(CNN)and a bi-directional Long Short-Term Memory(BiLSTM).Finally,we used pre-trained BERT,DistilBERT,and ULMFiT models in the third ***:BERTTimbau,a BERT architecture model pre-trained on a large Portuguese corpus,presented the best results for the task,even though with a performance of only 4%superior to a LinearSVC model using TF-IDF feature *** limitations:The dataset was highly imbalanced,as usual in patent applications,so the classes with the lowest samples were expected to present the worst *** result happened in some cases,especially in classes with less than 60 training *** implications:patent classification is challenging because of the hierarchical classification system,the context overlap,and the underrepresentation of the ***,the final model presented an acceptable performance given the size of the dataset and the task *** model can support the decision and improve the time by proposing a category in the second level of ICP,which is one of the critical phases of the grant patent ***/value:To our knowledge,the proposed models were never implemented for Portuguese patent classification.

关键词： Natural Language Processing(NLP) patent classification Transfer Learning Bi-directional Encoder Representations for Transformers(BERT)

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A patent Text classification Method Based on Phrase-Context Fusion Feature

A Patent Text Classification Method Based on Phrase-Context ...

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2021中国智能自动化大会(CIAC2021)

作者： Yuhui Wang Junping Du Yingxia Shao Ang Li Xin Xu Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications

The traditional patent classification depends on manual classification,which needs the staff to have a strong professional background and has low efficiency. The accuracy of classification is also greatly affected by the high similarity between the subdivision categories. Therefore, automatic classification of patents is needed to speed up the patent classification and improve the accuracy. In this paper, we present a phrase-context fusion feature(PCFF) method for patent classification. The convolutional neural network(CNN) extracts the phrase feature of the patent text, and the bi-directional long short-term memory(BiLSTM) units produce the contextual features. Furthermore, an attention mechanism is developed to adaptively select the contextual features and combines the high separable parts of the features extracted. PCFF model combines the advantages of the two networks and complements each other through fusing the feature from two networks through fusion attention. Experimental results demonstrate that the PCFF model produces complementary fusion feature and obtains better performance against the state-of-the-art baselines on the patent dataset.

关键词： patent classification Fusion feature Attention CNN LSTM

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