  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.
