NLP-CIC-WFU at SocialDisNER: Disease Mention Extraction in Spanish Tweets Using Transfer Learning and Search by Propagation
Authors:
Antonio Tamayo (ajtamayo2019@ipn.cic.mx, ajtamayoh@gmail.com)
Diego A. Burgos (burgosda@wfu.edu)
Alexander Gelbulkh (gelbukh@gelbukh.com)
For bugs or questions related to the code, do not hesitate to contact us (Antonio Tamayo: ajtamayoh@gmail.com)
If you use this code please cite our work:
Tamayo, A., Gelbukh, A., & Burgos, D. A. (2022, October). Nlp-cic-wfu at socialdisner: Disease mention extraction in spanish tweets using transfer learning and search by propagation. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task (pp. 19-22).
Named entity recognition (e.g., disease mention extraction) is one of the most relevant tasks for data mining in the medical field. Although it is a well-known challenge, the bulk of the efforts to tackle this task have been made using clinical texts commonly written in English. In this work, we present our contribution to the SocialDisNER competition, which consists of a transfer learning approach to extracting disease mentions in a corpus from Twitter written in Spanish. We fine-tuned a model based on mBERT and applied post-processing using regular expressions to propagate the entities identified by the model and enhance disease mention extraction. Our system achieved a competitive strict F1 of 0.851 on the testing data set.
Option 1: Hugging Face Space
Option 2: Hugging Face Model