nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Named Entity Recognition on CORD-19 Bio-Medical Dataset with Tolerance Rough Sets

verfasst von : Seeratpal Jaura, Sheela Ramanna

Erschienen in: Transactions on Rough Sets XXIII

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Biomedical named entity recognition is becoming increasingly important to biomedical research due to a proliferation of articles and also due to the current pandemic disease. This paper addresses the task of automatically finding and recognizing biomedical entity types related to COVID (e.g., virus, cell, therapeutic) with tolerance rough sets. The task includes i) extracting nouns and their co-occurring contextual patterns from a large BioNER dataset related to COVID-19 and, ii) annotating unlabelled data with a semi-supervised learning algorithm using co-occurence statistics. 465,250 noun phrases and 6,222,196 contextual patterns were extracted from 29,500 articles using natural language text processing methods. Three categories were successfully classified at this time: virus, cell and therapeutic. Early precision@N results demonstrate that our proposed tolerant pattern learner (TPL) is able to constrain concept drift in all 3 categories during the iterative learning process.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Fuzzy -Cut in Rough Sets and Its Application

Nächstes Kapitel Granularity and Rational Approximation: Rethinking Graded Rough Sets

https://www.nlm.nih.gov/bsd/pmresources.html.

http://lemurproject.org/clueweb09/.

Callan, J.: The lemur project and its clueweb12 dataset. In: Invited Talk at the SIGIR 2012 Workshop on Open-Source Information Retrieval (2012)

Cho, H., Lee, H.: Biomedical named entity recognition using deep neural networks with contextual information. BMC Bioinform. 20, 1–11 (2019). Article number: 735. https://doi.org/10.1186/s12859-019-3321-4

Ho, T.B., Nguyen, N.B.: Nonhierarchical document clustering based on a tolerance rough set model. Int. J. Intell. Syst. 17, 199–212 (2002)CrossRefMATH

Kawasaki, S., Binh, N., Bao, T.: Hierarchical document clustering based on tolerance rough set model. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 458–463. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45372-5_51CrossRef

Marcus, S.: Tolerance rough sets, Cech topologies, learning processes. Bull. Pol. Acad. Sci. Tech. Sci. 42(3), 471–487 (1994)MATH

Moghaddam, H.: Exploring scalability and concept drift issues in learning categorical facts with tolerance rough sets. Master’s thesis, University of Winnipeg (2019). Supervisor: S. Ramanna

Moghaddam, H., Ramanna, S.: Harvesting patterns from textual web sources with tolerance rough sets. Patterns 1(4), 100053 (2020)CrossRef

Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Linguisticae Investigationes 30(1), 3–26 (2007). http://www.ingentaconnect.com/content/jbp/li/2007/00000030/00000001/art00002

Ngo, C.L.: A tolerance rough set approach to clustering web search results. Master’s thesis, Warsaw University (2003)

10.

Nguyen, H.S.: Applications of tolerance rough set model semantic text analysis. In: Ropiak, K., Polkowski, L., Artiemjew, P. (eds.) Proceedings of the 28th International Workshop on Concurrency, Specification and Programming. CEUR Workshop Proceedings, Olsztyn, Poland, 24–26 September 2019, vol. 2571. CEUR-WS.org (2019). http://ceur-ws.org/Vol-2571/CSP2019_paper_18.pdf

11.

Nguyen, H.S., Ho, T.B.: Rough document clustering and the internet. In: Handbook of Granular Computing, pp. 987–1003 (2008)

12.

Nguyen, S.H., Nguyen, H.S.: An approach to semantic indexing based on tolerance rough set model. In: Nguyen, N.T., van Do, T., le Thi, H.A. (eds.) Advanced Computational Methods for Knowledge Engineering. SCI, vol. 479, pp. 343–354. Springer International Publishing, Heidelberg (2013). https://doi.org/10.1007/978-3-319-00293-4_26CrossRef

13.

Nieminen, J.: Rough tolerance equality and tolerance black boxes. Fund. Inform. 11, 289–296 (1988)MathSciNetMATH

14.

Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982). https://doi.org/10.1007/BF01001956CrossRefMATH

15.

Perera, N., Dehmer, M., Emmert-Streib, F.: Named entity recognition and relation detection for biomedical information extraction. Front. Cell Dev. Biol. 8, 673 (2020). https://www.frontiersin.org/article/10.3389/fcell.2020.00673

16.

Polkowski, L., Skowron, A., Zytkow, J.: Tolerance based rough sets. In: Lin, T.Y., Wildberger, M. (eds.) Soft Computing: Rough Sets, Fuzzy Logic, Neural Networks, Uncertainty Management, Knowledge Discovery, pp. 55–58. Simulation Councils Inc., San Diego (1994)

17.

Ramanna, S., Peters, J.F., Sengoz, C.: Application of tolerance rough sets in structured and unstructured text categorization: a survey. In: Wang, G., Skowron, A., Yao, Y., Ślęzak, D., Polkowski, L. (eds.) Thriving Rough Sets. SCI, vol. 708, pp. 119–138. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54966-8_6CrossRef

18.

Sengoz, C.: A granular-based approach for semi-supervised web information labeling. Master’s thesis, University of Winnipeg (2014). Supervisor: S. Ramanna

19.

Sengoz, C., Ramanna, S.: A semi-supervised learning algorithm for web information extraction with tolerance rough sets. In: Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, Y.-S. (eds.) AMT 2014. LNCS, vol. 8610, pp. 1–10. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09912-5_1CrossRef

20.

Sengoz, C., Ramanna, S.: Learning relational facts from the web: a tolerance rough set approach. Pattern Recogn. Lett. 67(P2), 130–137 (2015)CrossRef

21.

Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fund. Inform. 27(2–3), 245–253 (1996)MathSciNetMATH

22.

Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske skrifter, I kommission hos E. Munksgaard (1948). http://books.google.co.in/books?id=rpS8GAAACAAJ

23.

Swieboda, W., Krasuski, A., Nguyen, H.S., Janusz, A.: Interactive method for semantic document indexing based on explicit semantic analysis. Fund. Inform. 132(3), 423–438 (2014). https://doi.org/10.3233/FI-2014-1052CrossRef

24.

Świeboda, W., Meina, M., Nguyen, H.S.: Weight learning for document tolerance rough set model. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS (LNAI), vol. 8171, pp. 385–396. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41299-8_37CrossRef

25.

Virginia, G., Nguyen, H.S.: Lexicon-based document representation. Fundamenta Informatica 124(1–2), 27–46 (2013)MathSciNetCrossRefMATH

26.

Virginia, G., Nguyen, H.S.: A semantic text retrieval for Indonesian using tolerance rough sets models. In: Peters, J.F., Skowron, A., Ślȩzak, D., Nguyen, H.S., Bazan, J.G. (eds.) Transactions on Rough Sets XIX. LNCS, vol. 8988, pp. 138–224. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-47815-8_9CrossRef

27.

Wang, X., Song, X., Li, B., Guan, Y., Han, J.: Comprehensive named entity recognition on CORD-19 with distant or weak supervision. arXiv preprint arXiv:2003.12218 (2020)

Titel: Named Entity Recognition on CORD-19 Bio-Medical Dataset with Tolerance Rough Sets
verfasst von: Seeratpal Jaura
Sheela Ramanna
Verlag: Springer Berlin Heidelberg
Buch: Transactions on Rough Sets XXIII
Print ISBN: 978-3-662-66543-5

Electronic ISBN: 978-3-662-66544-2

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-662-66544-2_3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"