Patent classification
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Patent reviewers usually are responsible to classify the patent applications, i.e., they assign the design codes. This is time-consuming due to the numerous classification codes. For instance, the U.S. design patent system has 33 classes which are further divided into subclasses. Given that design patents include both titles and visual content, the goal of the experiment is patent classification by integrating titles, captions, and images. Although a single design patent can be associated with multiple design codes, we focus on the primary classification—solving the task as a multi-class classification problem.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Hope-Rita/EDGPAT
공식 코드Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction
https://github.com/ai-growth-lab/semp_patent
공식 코드A Survey on Sentence Embedding Models Performance for Patent Analysis
https://github.com/doribear/A-Novel-Patent-Similarity-Measurement-Methodology
공식 코드A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance
https://github.com/hope-rita/patcls
공식 코드Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification
https://github.com/jiehsheng/PatentBERT
공식 코드PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model
https://github.com/zoeyou/autotaxo
공식 코드Technological taxonomies for hypernym and hyponym retrieval in patent texts
https://github.com/daniel-hain/ML_course_maastricht
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.