Named Entity Recognition in Spanish Biomedical Literature: Short Review and Bert Model

Entity Recognition (NER) is the first step for knowledge acquisition when we deal with an unknown corpus of texts. Having received these entities, we have an opportunity to form parameters space and to solve problems of text mining as concept normalization, speech recognition, etc. The recent advances in NER are related to the technology of contextualized word embeddings, which transforms text to the form being effective for Deep Learning. In the paper, we show how NER model detects pharmacological substances, compounds, and proteins in the dataset obtained from the Spanish Clinical Case Corpus (SPACCC). To achieve this goal, we train from scratch the BERT language representation model and fine-tune it for our problem. As it is expected, this model shows better results than the NER model trained over the standard word embeddings. We further conduct an error analysis showing the origins of models' errors and proposing strategies to further improve the model's quality.