Three-Layer Retrieval and Self-Evaluation Classification Method Based on FastText Algorithm
Patent big data serves as a valuable scientific research source for technological innovation, enabling breakthroughs beyond existing knowledge and fostering disruptive ideas. One key challenge in this field is how to efficiently obtain patent documents quickly and accurately. This is a critical focu...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11003909/ |
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| author | Yidan Li Huanhuan Hong Luhong Wen |
| author_facet | Yidan Li Huanhuan Hong Luhong Wen |
| author_sort | Yidan Li |
| collection | DOAJ |
| description | Patent big data serves as a valuable scientific research source for technological innovation, enabling breakthroughs beyond existing knowledge and fostering disruptive ideas. One key challenge in this field is how to efficiently obtain patent documents quickly and accurately. This is a critical focus in the exploration of patent search methodologies. Our approach differs from conventional patent search processes. We have developed a three-level patent classification method that utilizes a multi-step search strategy with specific constraints, alongside an innovative classification system based on the FastText algorithm. By combining these techniques with an emphasis on recall ratio, we can test the efficacy of each level of the database boundaries. This enables swift identification of target patents and allows for focused screening in specific fields, providing robust support for technical or product innovation activities. Furthermore, we applied this method to the retrieval of DBDI patent data, which represents one of the three leading commercial direct ionization ion source technologies globally. Our classification results indicate a remarkable accuracy of 96.97%, reflecting a 21.97% improvement over the TextRNN_Att text algorithm. This effectively demonstrates the success of our proposed methodology. Overall, this study offers a theoretical framework for researching multi-level classification methods in logical retrieval and provides a practical foundation for classifying direct ionization and ionization technologies. |
| format | Article |
| id | doaj-art-e4ae6575fd494c2f80f45afcc0128dfb |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e4ae6575fd494c2f80f45afcc0128dfb2025-08-20T02:29:42ZengIEEEIEEE Access2169-35362025-01-0113871368714810.1109/ACCESS.2025.357010011003909Three-Layer Retrieval and Self-Evaluation Classification Method Based on FastText AlgorithmYidan Li0https://orcid.org/0009-0003-5156-4359Huanhuan Hong1https://orcid.org/0000-0002-7239-8113Luhong Wen2https://orcid.org/0000-0002-0018-7739Research Institute of Advanced Technologies, Ningbo University, Ningbo, ChinaResearch Institute of Advanced Technologies, Ningbo University, Ningbo, ChinaResearch Institute of Advanced Technologies, Ningbo University, Ningbo, ChinaPatent big data serves as a valuable scientific research source for technological innovation, enabling breakthroughs beyond existing knowledge and fostering disruptive ideas. One key challenge in this field is how to efficiently obtain patent documents quickly and accurately. This is a critical focus in the exploration of patent search methodologies. Our approach differs from conventional patent search processes. We have developed a three-level patent classification method that utilizes a multi-step search strategy with specific constraints, alongside an innovative classification system based on the FastText algorithm. By combining these techniques with an emphasis on recall ratio, we can test the efficacy of each level of the database boundaries. This enables swift identification of target patents and allows for focused screening in specific fields, providing robust support for technical or product innovation activities. Furthermore, we applied this method to the retrieval of DBDI patent data, which represents one of the three leading commercial direct ionization ion source technologies globally. Our classification results indicate a remarkable accuracy of 96.97%, reflecting a 21.97% improvement over the TextRNN_Att text algorithm. This effectively demonstrates the success of our proposed methodology. Overall, this study offers a theoretical framework for researching multi-level classification methods in logical retrieval and provides a practical foundation for classifying direct ionization and ionization technologies.https://ieeexplore.ieee.org/document/11003909/Patent retrievalmulti-step search strategytertiary classificationFastText classification algorithmDBDI |
| spellingShingle | Yidan Li Huanhuan Hong Luhong Wen Three-Layer Retrieval and Self-Evaluation Classification Method Based on FastText Algorithm IEEE Access Patent retrieval multi-step search strategy tertiary classification FastText classification algorithm DBDI |
| title | Three-Layer Retrieval and Self-Evaluation Classification Method Based on FastText Algorithm |
| title_full | Three-Layer Retrieval and Self-Evaluation Classification Method Based on FastText Algorithm |
| title_fullStr | Three-Layer Retrieval and Self-Evaluation Classification Method Based on FastText Algorithm |
| title_full_unstemmed | Three-Layer Retrieval and Self-Evaluation Classification Method Based on FastText Algorithm |
| title_short | Three-Layer Retrieval and Self-Evaluation Classification Method Based on FastText Algorithm |
| title_sort | three layer retrieval and self evaluation classification method based on fasttext algorithm |
| topic | Patent retrieval multi-step search strategy tertiary classification FastText classification algorithm DBDI |
| url | https://ieeexplore.ieee.org/document/11003909/ |
| work_keys_str_mv | AT yidanli threelayerretrievalandselfevaluationclassificationmethodbasedonfasttextalgorithm AT huanhuanhong threelayerretrievalandselfevaluationclassificationmethodbasedonfasttextalgorithm AT luhongwen threelayerretrievalandselfevaluationclassificationmethodbasedonfasttextalgorithm |