The tasks aims at classifying the location entities (that is, GPE, GEO and FAC entities) into four different classes of clinical relevance: (a) the patient’s place of origin; (b) the patient’s place of residence; (c) a place where the patient has travelled to or from; (d) a place where the patient has received medical attention. Only one label is possible for each annotation.
Publication
Salvador Lima-López,, Eulàlia Farré-Maduell, Vicent Briva-Iglesias, Luis Gasco-Sanchez, Martin Krallinger (2023) MEDDOPLACE Shared Task overview: recognition, normalization and classification of locations and patient movement in clinical texts. Procesamiento del Lenguaje Natural, Revista nº 71, septiembre de 2023, pp. 301-311.
Language
Spanish
URL Task
NLP topic
Abstract task
Year
2023
Publication link
Ranking metric
Micro F
Task results
| System | MicroF1 Sort ascending |
|---|---|
| SINAI | 0.7600 |
| NLP_URJC | 0.3200 |

