TY - JOUR
T1 - LinkMed
T2 - 2023 Iberian Languages Evaluation Forum, IberLEF 2023
AU - Muñoz-Castro, Carlos
AU - Carvallo, Andrés
AU - Rojas, Matías
AU - Aracena, Claudio
AU - Guerra, Rodrigo
AU - Pizarro, Benjamín
AU - Dunstan, Jocelyn
N1 - Publisher Copyright:
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2023
Y1 - 2023
N2 - Relation extraction is an essential component of Natural Language Processing (NLP) and significantly influences information retrieval and structured information extraction. Within clinical notes, the task is needed to establish connections among illnesses, therapies, indications, and other medical concepts. Motivated by the above, in this work, we propose a two-step model approach for entity linking; in the first step, we solve entity recognition, and in the second, a relation classification approach. We evaluated our approach in a Spanish corpus of the TESTLINK challenge in IberLEF2023 (Iberian Languages Evaluation Forum), comprising 81 clinical notes to train and 80 clinical notes to test. Our results show competitive performance with a precision of 0.47, recall of 0.43, and F1-score of 0.45, presenting an effective strategy for relation extraction from clinical notes in Spanish.
AB - Relation extraction is an essential component of Natural Language Processing (NLP) and significantly influences information retrieval and structured information extraction. Within clinical notes, the task is needed to establish connections among illnesses, therapies, indications, and other medical concepts. Motivated by the above, in this work, we propose a two-step model approach for entity linking; in the first step, we solve entity recognition, and in the second, a relation classification approach. We evaluated our approach in a Spanish corpus of the TESTLINK challenge in IberLEF2023 (Iberian Languages Evaluation Forum), comprising 81 clinical notes to train and 80 clinical notes to test. Our results show competitive performance with a precision of 0.47, recall of 0.43, and F1-score of 0.45, presenting an effective strategy for relation extraction from clinical notes in Spanish.
KW - Clinical Text
KW - Link prediction
KW - Named Entity Recognition
KW - Natural Language Processing
UR - https://www.scopus.com/pages/publications/85175300943
M3 - Conference article
AN - SCOPUS:85175300943
SN - 1613-0073
VL - 3496
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 26 September 2023
ER -