| Autor: |
Lithgow-Serrano O; Dalle Molle Institute for Artificial Intelligence Research, IDSIA USI-SUPSI, Polo universitario Lugano-Campus Est, Via la Santa 1, CH-6962 Lugano, Switzerland., Cornelius J; Dalle Molle Institute for Artificial Intelligence Research, IDSIA USI-SUPSI, Polo universitario Lugano-Campus Est, Via la Santa 1, CH-6962 Lugano, Switzerland., Kanjirangat V; Dalle Molle Institute for Artificial Intelligence Research, IDSIA USI-SUPSI, Polo universitario Lugano-Campus Est, Via la Santa 1, CH-6962 Lugano, Switzerland., Méndez-Cruz CF; Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Avenida Universidad s/n Col. Chamilpa, 62210 Cuernavaca, Mor., Mexico., Rinaldi F; Dalle Molle Institute for Artificial Intelligence Research, IDSIA USI-SUPSI, Polo universitario Lugano-Campus Est, Via la Santa 1, CH-6962 Lugano, Switzerland. |
| Abstrakt: |
Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) Clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene's Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE's origin was useful to classify document types and NE's type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed. |
