Title: Deep Learning System for Biomedical Relation Extraction Combining External Sources of Knowledge
Speaker: Diana Sousa (LASIGE/DI)
When: March 11 (Thursday) at 12:00
Successful biomedical relation extraction can provide evidence to researchers about possible unknown associations between entities, advancing our current knowledge about those entities and their inherent processes. Multiple relation extraction approaches have been proposed to identify relations between literature concepts, namely using neural networks algorithms. However, most approaches do not use external sources of knowledge as added resources, such as domain-specific ontologies. Moreover, dataset availability is still limited to train reliable models. In this seminar, I will present the advantages of integrating multiple ontologies into a BI-LSTM deep learning system. Additionally, how to create high-quality biomedical training corpora by allying distant supervision with crowdsourcing. The ultimate goal of the thesis is to use external semantic sources of knowledge along with the latest state-of-the-art language representations to improve the current performance of biomedical relation extraction both in English and non-English languages.