Abstract
AbstractHere, we introduce ITEXT-BIO, an intelligent process for biomedical domain terminology extraction from textual documents and subsequent analysis. The proposed methodology consists of two complementary approaches, including free and driven term extraction. The first is based on term extraction with statistical measures, while the second considers morphosyntactic variation rules to extract term variants from the corpus. The combination of two term extraction and analysis strategies is the keystone of ITEXT-BIO. These include combined intra-corpus strategies that enable term extraction and analysis either from a single corpus (intra), or from corpora (inter). We assessed the two approaches, the corpus or corpora to be analysed and the type of statistical measures used. Our experimental findings revealed that the proposed methodology could be used: (1) to efficiently extract representative, discriminant and new terms from a given corpus or corpora, and (2) to provide quantitative and qualitative analyses on these terms regarding the study domain.
Publisher
Springer Science and Business Media LLC
Reference44 articles.
1. Arsevska E, Valentin S, Rabatel J, de Goër de Hervé J, Falala S, Lancelot R, Roche M. Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System. PLOS ONE. 2018;13(8):e0199960. https://doi.org/10.1371/journal.pone.0199960.
2. Azarafza M, Feizi-Derakhshi MR, Shendi MB. Textrank-based microblogs keyword extraction method for Persian language. Conference: 3rd International Congress on Science and Engineering, Hamburg, Germany, 2020
3. Bodenreider O. The unified medical language system (umls): integrating biomedical terminology. Nucleic Acids Res. 2004;32(suppl 1):D267–70.
4. Bracewell DB, Ren F, Kuriowa S. Multilingual single document keyword extraction for information retrieval. In: 2005 International Conference on Natural Language Processing and Knowledge Engineering, IEEE, 2005, pp 517–522
5. Brill E (1992) A simple rule-based part of speech tagger. In: Proceedings of the third conference on applied natural language processing, Association for Computational Linguistics, USA, ANLC ’92, pp. 152–155. https://doi.org/10.3115/974499.974526
Cited by
2 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