Research on named entity recognition method of marine natural products based on attention mechanism
Copyright © 2023 Ma, Yu, Gao, Wei, Xia, Wang and Liu..
Marine natural product (MNP) entity property information is the basis of marine drug development, and this entity property information can be obtained from the original literature. However, the traditional methods require several manual annotations, the accuracy of the model is low and slow, and the problem of inconsistent lexical contexts cannot be solved well. In order to solve the aforementioned problems, this study proposes a named entity recognition method based on the attention mechanism, inflated convolutional neural network (IDCNN), and conditional random field (CRF), combining the attention mechanism that can use the lexicality of words to make attention-weighted mentions of the extracted features, the ability of the inflated convolutional neural network to parallelize operations and long- and short-term memory, and the excellent learning ability. A named entity recognition algorithm model is developed for the automatic recognition of entity information in the MNP domain literature. Experiments demonstrate that the proposed model can properly identify entity information from the unstructured chapter-level literature and outperform the control model in several metrics. In addition, we construct an unstructured text dataset related to MNPs from an open-source dataset, which can be used for the research and development of resource scarcity scenarios.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Frontiers in chemistry - 11(2023) vom: 21., Seite 958002 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ma, Xiaodong [VerfasserIn] |
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| Links: |
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| Themen: |
Attention mechanism |
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| Anmerkungen: |
Date Revised 28.02.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fchem.2023.958002 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM353529818 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2023 Ma, Yu, Gao, Wei, Xia, Wang and Liu. | ||
| 520 | |a Marine natural product (MNP) entity property information is the basis of marine drug development, and this entity property information can be obtained from the original literature. However, the traditional methods require several manual annotations, the accuracy of the model is low and slow, and the problem of inconsistent lexical contexts cannot be solved well. In order to solve the aforementioned problems, this study proposes a named entity recognition method based on the attention mechanism, inflated convolutional neural network (IDCNN), and conditional random field (CRF), combining the attention mechanism that can use the lexicality of words to make attention-weighted mentions of the extracted features, the ability of the inflated convolutional neural network to parallelize operations and long- and short-term memory, and the excellent learning ability. A named entity recognition algorithm model is developed for the automatic recognition of entity information in the MNP domain literature. Experiments demonstrate that the proposed model can properly identify entity information from the unstructured chapter-level literature and outperform the control model in several metrics. In addition, we construct an unstructured text dataset related to MNPs from an open-source dataset, which can be used for the research and development of resource scarcity scenarios | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a attention mechanism | |
| 650 | 4 | |a conditional random field | |
| 650 | 4 | |a inflated convolutional neural network | |
| 650 | 4 | |a marine natural products | |
| 650 | 4 | |a named entity recognition | |
| 700 | 1 | |a Yu, Rilei |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Chunxiao |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Zhiqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Yimin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaowei |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hao |e verfasserin |4 aut | |
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