Academic Journal
Text analytics methods for automatic annotation of scientific documents.
| Τίτλος: | Text analytics methods for automatic annotation of scientific documents. |
|---|---|
| Συγγραφείς: | Tanirbergenov, Adilbek, Akhmetzhanov, Madi, Taszhurekova, Zhazira, Khassanova, Munaram, Tassuov, Bolat |
| Πηγή: | International Journal of Innovative Research & Scientific Studies; 2025, Vol. 8 Issue 4, p491-499, 9p |
| Θεματικοί όροι: | Machine learning, Automatic summarization, Natural language processing, Hybrid systems, Text mining, Annotations |
| Περίληψη: | This study aims to develop a hybrid system for the automatic annotation of scientific texts that efficiently processes multilingual publications using state-of-the-art natural language processing (NLP) technologies. The system integrates classical algorithms (Gensim, NLTK) with transformer-based models via the Cohere API to achieve high semantic consistency and accuracy in annotations. The system architecture comprises modules for data acquisition, preprocessing, manual and automatic annotation, data storage, and quality control. The performance of the proposed model was benchmarked against established methods such as BERTSUM, TF-IDF + LSA, and GPT-3.5-turbo using evaluation metrics including ROUGE, BLEU, and METEOR. The hybrid model outperformed other automated systems, demonstrating superior scores across ROUGE-1 (0.52), BLEU (0.41), and METEOR (0.39) metrics, indicating its effectiveness in producing concise and semantically accurate summaries. The system also achieved 100% language detection accuracy and 90% accuracy in semantic word relationships via Word2Vec. The integration of traditional statistical methods with advanced transformer models enables the proposed system to deliver high-quality annotations suitable for diverse scientific domains. The results validate the model's ability to process and summarize complex scientific texts effectively. This system provides a scalable, secure, and user-friendly platform for researchers, institutions, and developers. It supports multilingual annotation, seamless API integration, and potential deployment in cloud environments, offering significant benefits for academic, biomedical, and information-intensive sectors. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Innovative Research & Scientific Studies is the property of International Journal of Innovative Research & Scientific Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 186937127 RelevancyScore: 1023 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1023.09039306641 |
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| Items | – Name: Title Label: Title Group: Ti Data: Text analytics methods for automatic annotation of scientific documents. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tanirbergenov%2C+Adilbek%22">Tanirbergenov, Adilbek</searchLink><br /><searchLink fieldCode="AR" term="%22Akhmetzhanov%2C+Madi%22">Akhmetzhanov, Madi</searchLink><br /><searchLink fieldCode="AR" term="%22Taszhurekova%2C+Zhazira%22">Taszhurekova, Zhazira</searchLink><br /><searchLink fieldCode="AR" term="%22Khassanova%2C+Munaram%22">Khassanova, Munaram</searchLink><br /><searchLink fieldCode="AR" term="%22Tassuov%2C+Bolat%22">Tassuov, Bolat</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Innovative Research & Scientific Studies; 2025, Vol. 8 Issue 4, p491-499, 9p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+summarization%22">Automatic summarization</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Hybrid+systems%22">Hybrid systems</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Annotations%22">Annotations</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study aims to develop a hybrid system for the automatic annotation of scientific texts that efficiently processes multilingual publications using state-of-the-art natural language processing (NLP) technologies. The system integrates classical algorithms (Gensim, NLTK) with transformer-based models via the Cohere API to achieve high semantic consistency and accuracy in annotations. The system architecture comprises modules for data acquisition, preprocessing, manual and automatic annotation, data storage, and quality control. The performance of the proposed model was benchmarked against established methods such as BERTSUM, TF-IDF + LSA, and GPT-3.5-turbo using evaluation metrics including ROUGE, BLEU, and METEOR. The hybrid model outperformed other automated systems, demonstrating superior scores across ROUGE-1 (0.52), BLEU (0.41), and METEOR (0.39) metrics, indicating its effectiveness in producing concise and semantically accurate summaries. The system also achieved 100% language detection accuracy and 90% accuracy in semantic word relationships via Word2Vec. The integration of traditional statistical methods with advanced transformer models enables the proposed system to deliver high-quality annotations suitable for diverse scientific domains. The results validate the model's ability to process and summarize complex scientific texts effectively. This system provides a scalable, secure, and user-friendly platform for researchers, institutions, and developers. It supports multilingual annotation, seamless API integration, and potential deployment in cloud environments, offering significant benefits for academic, biomedical, and information-intensive sectors. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Innovative Research & Scientific Studies is the property of International Journal of Innovative Research & Scientific Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.53894/ijirss.v8i4.7876 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 491 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Automatic summarization Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Hybrid systems Type: general – SubjectFull: Text mining Type: general – SubjectFull: Annotations Type: general Titles: – TitleFull: Text analytics methods for automatic annotation of scientific documents. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tanirbergenov, Adilbek – PersonEntity: Name: NameFull: Akhmetzhanov, Madi – PersonEntity: Name: NameFull: Taszhurekova, Zhazira – PersonEntity: Name: NameFull: Khassanova, Munaram – PersonEntity: Name: NameFull: Tassuov, Bolat IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 26176548 Numbering: – Type: volume Value: 8 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Innovative Research & Scientific Studies Type: main |
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