This paper is in the following e-collection/theme issue:

Natural Language Processing (801)

Published on in Vol 8, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18953, first published .
The Impact of Pretrained Language Models on Negation and Speculation Detection in Cross-Lingual Medical Text: Comparative Study

The Impact of Pretrained Language Models on Negation and Speculation Detection in Cross-Lingual Medical Text: Comparative Study

The Impact of Pretrained Language Models on Negation and Speculation Detection in Cross-Lingual Medical Text: Comparative Study

Authors of this article:

Renzo Rivera Zavala1, 2 Author Orcid Image ;   Paloma Martinez1 Author Orcid Image
Article Authors Cited by (10) Tweetations (8) Metrics

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  2. Rivera-Zavala R, Martínez P. Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization. BMC Bioinformatics 2021;22(S1) View
  3. Stortenbeker I, Salm L, olde Hartman T, Stommel W, Das E, van Dulmen S. Coding linguistic elements in clinical interactions: a step-by-step guide for analyzing communication form. BMC Medical Research Methodology 2022;22(1) View
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  5. Weng K, Liu C, Chen C. Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study. JMIR Medical Informatics 2023;11:e46348 View
  6. Abbasi N, Lacson R, Kapoor N, Licaros A, Guenette J, Burk K, Hammer M, Desai S, Eappen S, Saini S, Khorasani R. Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging. American Journal of Roentgenology 2023;221(3):377 View
  7. Argüello-González G, Aquino-Esperanza J, Salvador D, Bretón-Romero R, Del Río-Bermudez C, Tello J, Menke S. Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network. BMC Medical Informatics and Decision Making 2023;23(1) View
  8. Perez N, Cuadros M, Rigau G. Negation and speculation processing: A study on cue-scope labelling and assertion classification in Spanish clinical text. Artificial Intelligence in Medicine 2023;145:102682 View
  9. Campillos-Llanos L, Valverde-Mateos A, Capllonch-Carrión A. Hybrid natural language processing tool for semantic annotation of medical texts in Spanish. BMC Bioinformatics 2025;26(1) View
  10. González-Moreno A, Ramos-González A, González-Carrasco I, Alonso Díaz de Durana M, Sellers Gutiérrez-Argumosa B, Moncada Salinero A, Pastor-Magro A, González-Piñeiro B, Tejedor-Alonso M, Martínez P. A clinical narrative corpus on nut allergy: annotation schema, guidelines and use case. Scientific Data 2025;12(1) View