In the realm of patent document analysis, assessing semantic similarity
between phrases presents a significant challenge, notably amplifying the
inherent complexities of Cooperative Patent Classification (CPC) research.
Firstly, this study addresses these challenges, recognizing early CPC work
while acknowledging past struggles with language barriers and document
intricacy. Secondly, it underscores the persisting difficulties of CPC
research.
To overcome these challenges and bolster the CPC system, This paper presents
two key innovations. Firstly, it introduces an ensemble approach that
incorporates four BERT-related models, enhancing semantic similarity accuracy
through weighted averaging. Secondly, a novel text preprocessing method
tailored for patent documents is introduced, featuring a distinctive input
structure with token scoring that aids in capturing semantic relationships
during CPC context training, utilizing BCELoss. Our experimental findings
conclusively establish the effectiveness of both our Ensemble Model and novel
text processing strategies when deployed on the U.S. Patent Phrase to Phrase
Matching dataset.

本研究探讨专利文件分析中的语义相似性评估，介绍了一种集成方法和专利文件的文本预处理方法，重点解决了语言障碍和文档复杂性问题，并在美国专利短语匹配数据集上证明了其有效性。

使用集成 BERT 相关模型和新型文本处理方法进行专利文档的语义相似性匹配

Semantic Similarity Matching for Patent Documents Using Ensemble  BERT-related Model and Novel Text Processing Method

This paper presents an automatic approach to creating taxonomies of technical
terms based on the Cooperative Patent Classification (CPC). The resulting
taxonomy contains about 170k nodes in 9 separate technological branches and is
freely available. We also show that a Text-to-Text Transfer Transformer (T5)
model can be fine-tuned to generate hypernyms and hyponyms with relatively high
precision, confirming the manually assessed quality of the resource. The T5
model opens the taxonomy to any new technological terms for which a hypernym
can be generated, thus making the resource updateable with new terms, an
essential feature for the constantly evolving field of technological
terminology.

本文提出了一种基于专利合作分类（CPC）的技术术语分类自动化方法，生成的分类包含 9 个独立技术分支中的约 170k 个节点，并且使用 Text-to-Text Transfer Transformer (T5) 模型 fine-tune 生成上下位词并得到相对较高的准确性，从而证明了此资源的质量。T5 模型可以打开新的技术术语的分类（根据上下位关系），因此这个资源可以更新，是技术术语领域不断发展的必要特性。