SEARCHFORMER: Semantic patent embeddings by siamese transformers for prior art search-Reference-Cited by-同舟云学术

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SEARCHFORMER: Semantic patent embeddings by siamese transformers for prior art search

Published:2023-06 Issue: Volume:73 Page:102192
ISSN:0172-2190
Container-title:World Patent Information
language:en
Short-container-title:World Patent Information

Author:

Vowinckel Konrad,Hähnke Volker D.^ORCID

Publisher

Elsevier BV

Subject

Library and Information Sciences,Computer Science Applications,Process Chemistry and Technology,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment,Bioengineering

Reference59 articles.

1. Attention is all you need;Vaswani,2017

2. BERT: Pre-training of deep bidirectional transformers for language understanding;Devlin,2019

3. End-to-end open-domain question answering with BERTserini;Yang,2019

4. How to fine-tune BERT for text classification?;Sun,2019

5. Text summarization with pretrained encoders;Liu,2019

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel re-ranking architecture for patent search;World Patent Information;2024-09

2. Is your search query well-formed? A natural query understanding for patent prior art search;World Patent Information;2024-03

3. Artificial Intelligence Reducing the Intricacies of Patent Prior Art Search;2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES);2023-04-28

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