Annotating and normalizing biomedical NEs with limited knowledge

Named entity recognition (NER) is the very first step in the linguistic processing of any new domain. It is currently a common process in BioNLP on English clinical text. However, it is still in its infancy in other major languages, as it is the case for Spanish. Presented under the umbrella of the PharmaCoNER shared task, this paper describes a very simple method for the annotation and normalization of pharmacological, chemical and, ultimately, biomedical named entities in clinical cases. The system developed for the shared task is based on limited knowledge, collected, structured and munged in a way that clearly outperforms scores obtained by similar dictionary-based systems for English in the past. Along with this recovering of the knowledge-based methods for NER in subdomains, the paper also highlights the key contribution of resource-based systems in the validation and consolidation of both the annotation guidelines and the human annotation practices. In this sense, some of the authors discoverings on the overall quality of human annotated datasets question the above-mentioned `official' results obtained by this system, that ranked second (0.91 F1-score) and first (0.916 F1-score), respectively, in the two PharmaCoNER subtasks.