LASIGE and UNICAGE solution to the NASA LitCoin NLP Competition
Pedro Ruas Diana F. Sousa André Neves Carlos Cruz Francisco M. Couto
Biomedical Natural Language Processing (NLP) tends to become cumbersome for
most researchers, frequently due to the amount and heterogeneity of text to be
processed. To address this challenge, the industry is continuously developing
highly efficient tools and creating more flexible engineering solutions. This
work presents the integration between industry data engineering solutions for
efficient data processing and academic systems developed for Named Entity
Recognition (LasigeUnicage\_NER) and Relation Extraction (BiOnt). Our design
reflects an integration of those components with external knowledge in the form
of additional training data from other datasets and biomedical ontologies. We
used this pipeline in the 2022 LitCoin NLP Challenge, where our team
LasigeUnicage was awarded the 7th Prize out of approximately 200 participating
teams, reflecting a successful collaboration between the academia (LASIGE) and
the industry (Unicage). The software supporting this work is available at
\url{https://github.com/lasigeBioTM/Litcoin-Lasige_Unicage}.