Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model
Abstract Background Biomedical text mining is a technique that extracts essential information from scientific articles using named entity recognition (NER). Traditional NER methods rely on dictionaries, rules, or curated corpora, which may not always be accessible. To overcome these challenges, deep...
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BMC
2025-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-06008-w |
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| author | Ram Chandra Bhushan Rakesh Kumar Donthi Yojitha Chilukuri Ulligaddala Srinivasarao Polisetty Swetha |
| author_facet | Ram Chandra Bhushan Rakesh Kumar Donthi Yojitha Chilukuri Ulligaddala Srinivasarao Polisetty Swetha |
| author_sort | Ram Chandra Bhushan |
| collection | DOAJ |
| description | Abstract Background Biomedical text mining is a technique that extracts essential information from scientific articles using named entity recognition (NER). Traditional NER methods rely on dictionaries, rules, or curated corpora, which may not always be accessible. To overcome these challenges, deep learning (DL) methods have emerged. However, DL-based NER methods may need help identifying long-distance relationships within text and require significant annotated datasets. Results This research has proposed a novel model to address the challenges in natural language processing. The Improved Green anaconda-assisted Bi-GRU based Hierarchical ResNet BNER model (IGa-BiHR BNERM) is the model. IGa-BiHR BNERM model has shown promising results in accurately identifying named entities. The MACCROBAT dataset was obtained from Kaggle and underwent several pre-processing steps such as Stop Word Filtering, WordNet processing, Removal of non-alphanumeric characters, stemming Segmentation, and Tokenization, which is standardized and improves its quality. The pre-processed text was fed into a feature extraction model like the Robustly Optimized BERT –Whole Word Masking model. This model provides word embeddings with semantic information. Then, the BNER process utilized an Improved Green Anaconda-assisted Bi-GRU-based Hierarchical ResNet BNER model (IGa-BiHR BNERM). Conclusion To improve the training phase of the IGa-BiHR BNERM, the Improved Green Anaconda Optimization technique was used to select optimal weight parameter coefficients for training the model parameters. After the model was tested using the MACCROBAT dataset, it outperformed previous models with a tremendous accuracy rate of 99.11%. This model effectively and accurately identifies biomedical names within the text, significantly advancing this field. |
| format | Article |
| id | doaj-art-c872bdf1bc914a2c910b14a087c56825 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-c872bdf1bc914a2c910b14a087c568252025-02-02T12:45:00ZengBMCBMC Bioinformatics1471-21052025-01-0126113410.1186/s12859-024-06008-wBiomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet modelRam Chandra Bhushan0Rakesh Kumar Donthi1Yojitha Chilukuri2Ulligaddala Srinivasarao3Polisetty Swetha4Software Architect, Alstom Transport India LimitedDepartment of CSE GITAM (Deemed to be) UNIVERSITY HyderabadSt. Jude Childrens Cancer Research HospitalDepartment of CSE GITAM (Deemed to be) UNIVERSITY HyderabadDepartment of Information Technology, Vardhaman College of EngineeringAbstract Background Biomedical text mining is a technique that extracts essential information from scientific articles using named entity recognition (NER). Traditional NER methods rely on dictionaries, rules, or curated corpora, which may not always be accessible. To overcome these challenges, deep learning (DL) methods have emerged. However, DL-based NER methods may need help identifying long-distance relationships within text and require significant annotated datasets. Results This research has proposed a novel model to address the challenges in natural language processing. The Improved Green anaconda-assisted Bi-GRU based Hierarchical ResNet BNER model (IGa-BiHR BNERM) is the model. IGa-BiHR BNERM model has shown promising results in accurately identifying named entities. The MACCROBAT dataset was obtained from Kaggle and underwent several pre-processing steps such as Stop Word Filtering, WordNet processing, Removal of non-alphanumeric characters, stemming Segmentation, and Tokenization, which is standardized and improves its quality. The pre-processed text was fed into a feature extraction model like the Robustly Optimized BERT –Whole Word Masking model. This model provides word embeddings with semantic information. Then, the BNER process utilized an Improved Green Anaconda-assisted Bi-GRU-based Hierarchical ResNet BNER model (IGa-BiHR BNERM). Conclusion To improve the training phase of the IGa-BiHR BNERM, the Improved Green Anaconda Optimization technique was used to select optimal weight parameter coefficients for training the model parameters. After the model was tested using the MACCROBAT dataset, it outperformed previous models with a tremendous accuracy rate of 99.11%. This model effectively and accurately identifies biomedical names within the text, significantly advancing this field.https://doi.org/10.1186/s12859-024-06008-wBiomedical nameRecognitionWord embeddingROBERT-WWMBi-GRUHierarchical ResNet |
| spellingShingle | Ram Chandra Bhushan Rakesh Kumar Donthi Yojitha Chilukuri Ulligaddala Srinivasarao Polisetty Swetha Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model BMC Bioinformatics Biomedical name Recognition Word embedding ROBERT-WWM Bi-GRU Hierarchical ResNet |
| title | Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model |
| title_full | Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model |
| title_fullStr | Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model |
| title_full_unstemmed | Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model |
| title_short | Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model |
| title_sort | biomedical named entity recognition using improved green anaconda assisted bi gru based hierarchical resnet model |
| topic | Biomedical name Recognition Word embedding ROBERT-WWM Bi-GRU Hierarchical ResNet |
| url | https://doi.org/10.1186/s12859-024-06008-w |
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