Methods of extracting biomedical information from patents and scientific publications (on the example of chemical compounds)

Мұқаба

Дәйексөз келтіру

Толық мәтін

Аннотация

This article proposes an algorithm for solving the problem of extracting information from biomedical patents and scientific publications. The introduced algorithm is based on machine learning methods. Experiments were carried out on patents from the USPTO database. Experiments have shown that the best extraction quality was achieved by a model based on BioBERT.

Авторлар туралы

N. Kolpakov

Moscow Institute of Physics and Technology

Email: kolpakov.na@phystech.edu

Bachelor

Ресей, 1A, building 1, Kerch str., Moscow, 117303 Moscow

A. Molodchenkov

Federal research center “Computer science and control” of Russian Academy of Sciences; Peoples’ Friendship University of Russia

Хат алмасуға жауапты Автор.
Email: aim@tesyan.ru

PhD

Ресей, 44/2 Vavilova str., Moscow, 119333; 6, Miklukho-Maklaya str., Moscow, 117198

A. Lukin

Peoples’ Friendship University of Russia

Email: antonvlukin@gmail.com

учёная степень

Ресей, 6, Miklukho-Maklaya str., Moscow, 117198

Әдебиет тізімі

  1. Akhondi, S., Rey, H., Schwörer, M., Maier, M., Toomey, J., Nau, H., Ilchmann, G., Sheehan, M., Irmer, M., Bobach, C., Doornenbal, M., Gregory and M., Kors, J. 2019. Automatic identification of relevant chemical compounds from patents. Database: the journal of biological databases and curation, vol. 1, pp. 1–14.
  2. Jessop, D., Adams, S., Willighagen, E., Hawizy, L. and Murray-Rust, P. 2011. OSCAR4: A flexible architecture for chemical textmining. Journal of cheminformatics, vol. 3, no. 1, pp. 1–12.
  3. Soysal, E., Wang, J., Jiang, M., Wu, Y., Pakhomov, S., Liu, H. and Qi, W. 2018. CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines. Journal of the American Medical Informatics Association: JAMIA, vol. 25, no. 3, pp. 331–336.
  4. Swain, M. and Cole, J. 2016. ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature. Journal of Chemical Information and Modeling, vol. 56, no. 10, pp. 1894–1904.
  5. Jinhyuk, L., Wonjin, Y., Sungdong, K., Donghyeon, K., Sunkyu, K., Chan, H. S. and Jaewoo, K. 2019. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, vol. 36, no. 4, pp. 1234–1240.
  6. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L. and Polosukhin, I. 2017. Attention Is All You Need. Advances in Neural Information Processing Systems, vol. 30, pp. 5998–6008.
  7. Devlin, J., Chang, M.-W., Lee, K. and Toutanova, K. 2019. Bert: pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 4171–4186.
  8. The OpenNLP Project. Available at: http://opennlp. apache.org (accessed February 20, 2022).
  9. CRFsuite: a Fast Implementation of Conditional Random Fields (CRFs). Available at: http://www. chokkan.org/software/crfsuite/ (accessed February 20, 2022).
  10. Barnard, J. 1991. A comparison of different approaches to Markush structure handling. Journal of Chemical Information and Computer Sciences, vol. 31, no. 1, pp. 64–68.
  11. Heller, S., McNaught, A., Pletnev, I., Stein, S. and Tchekhovskoi, D. 2015. The IUPAC International Chemical Identifier. Journal of Cheminformatics, vol. 7, pp. 1–34.
  12. USPTO. Available at: https://www.uspto.gov/ patents (accessed February 20, 2022).
  13. Mikolov, T., Chen, K., Corrado, G. and Dean, J. 2013. Efficient Estimation of Word Representations in Vector Space. Proceedings of Workshop at ICLR, pp. 1–12.
  14. Mikolov, T., Yih, W.-T. and Zweig, G. 2013. Linguistic regularities in continuous space word representations. Proceedings of NAACL-HLT, pp. 746–751.
  15. Cortes, C. and Vapnik, V. 1995. Support-vector networks. Machine Learning, vol. 20, no. 3, pp. 273–297.
  16. Finkel, J., Grenager, T. and Manning, C. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363–370.
  17. Mitchell, T. 1997. Machine Learning. New York: McGraw-Hill. 432 p.

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML
Жүктеу

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).