The recognition of pharmacological substances, compounds and proteins is an
essential preliminary work for the recognition of relations between chemicals
and other biomedically relevant units. In this paper, we describe an approach
to Task 1 of the PharmaCoNER Challenge, which involves the recognition of
mentions of chemicals and drugs in Spanish medical texts. We train a
state-of-the-art BiLSTM-CRF sequence tagger with stacked Pooled Contextualized
Embeddings, word and sub-word embeddings using the open-source framework FLAIR.
We present a new corpus composed of articles and papers from Spanish health
science journals, termed the Spanish Health Corpus, and use it to train
domain-specific embeddings which we incorporate in our model training. We
achieve a result of 89.76% F1-score using pre-trained embeddings and are able
to improve these results to 90.52% F1-score using specialized embeddings.

本研究基于 PharmaCoNER 挑战任务 1，使用 BiLSTM-CRF 序列标记器和基于 FLAIR 的深度学习方法，通过构建一个新的 Spanish Health Corpus 并使用领域专用嵌入，成功提高了化学药品和药物的识别精度。