Summary of Semantic Similarity Matching For Patent Documents Using Ensemble Bert-related Model and Novel Text Processing Method, by Liqiang Yu et al.
Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method
by Liqiang Yu, Bo Liu, Qunwei Lin, Xinyu Zhao, Chang Che
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study tackles the intricate problem of assessing semantic similarity between phrases in patent documents, a crucial task for Cooperative Patent Classification (CPC) research. By recognizing the limitations of past work and acknowledging the complexities of patent document analysis, this study aims to overcome language barriers and document intricacy. The authors highlight the persisting difficulties of CPC research, emphasizing the need for innovative solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to compare ideas in patents that are written in different languages. It’s a big challenge because patent documents can be very complicated and hard to understand. The researchers want to make it easier to analyze these documents by comparing the meanings of phrases inside them. They’re trying to solve this problem so that people can better organize and search for information in patents. |
Keywords
» Artificial intelligence » Classification
