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Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification
The paper describes the participation of the Lasige-BioTM team at sub-tracks A and B of ProfNER, which was based on: i) a BiLSTM-CRF model that leverages contextual and classical word embeddings to recognize and classify the mentions, and ii) on a rule-based module to classify tweets.
COVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities
With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher.
LASIGE and UNICAGE solution to the NASA LitCoin NLP Competition
Biomedical Natural Language Processing (NLP) tends to become cumbersome for most researchers, frequently due to the amount and heterogeneity of text to be processed.
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