Márcia Barros, Pedro Ruas, Diana Sousa, and Francisco M. Couto
participated in the Biomedical Linked Annotation Hackathon 7
with the project entitled: Annotating a multilingual COVID-19-related corpus
https://blah7.linkedannotation.org/projects%23h.hgnn9hk4slfp#h.hgnn9hk4slfp
Title: Validation of Automatic Similarity Measures
Who: Pedro Jorge Mendes dos Santos
When: January 8, 2021, 2.00pm
Where: FCUL
Title: Why analyze biomedical text if we have data?
Speaker: Francisco Couto (LASIGE/DI-FCUL)
Date: December 18, 14h30
Where: https://videoconf-colibri.zoom.us/my/tjvguerreiro
Organized by: Tiago Guerreiro
Data Science Seminars is a Ciências ULisboa Master Programme in Data Science’s course that offers an overview of data science, with a focus in its application areas.
A hybrid approach toward biomedical relation extraction training corpora: combining distant supervision with crowdsourcing by Diana Sousa, Andre Lamurias, and Francisco M. Couto was published in the journal Database.
The article “COVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities”, co-authored by Marcia Afonso Barros, Andre Lamurias, Diana Sousa, Pedro Ruas, Francisco M. Couto was accepted and presented at the proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020.
The main contributions of this work are: a dataset of 9k articles automatically annotated with relevant items/concepts for CORD19; a sample dataset curated for CORD-19; a sample dataset with relations between the entities of the four ontologies; an implicit feedback matrix based on the previous datasets.
The work is freely available at: https://github.com/lasigeBioTM/knowledge-extraction-from-CORD-19
Title: Recommender Systems Based on Deep Learning Techniques
Who: Fábio Colaço
When: November 12, 2020, 5.00pm
Where: FCUL
Title: Applying Deep Learning Extreme Multi-Label Classification to the Biomedical and Multilingual Panorama
Who: André Neves
When: November 3, 2020, 3.00pm
Where: FCUL
Video describing our participation in CANTEMIST (CANcer TExt Mining Shared Task – tumor named entity recognition):
Results: