Student Research Abstract: Dual Architecture for Name Entity Extraction and Relation Extraction with Applications in Medical Corpora

There is a growing interest in automatic knowledge discovery in plain text documents. Automation enables the analysis of massive collections of information. Such efforts are relevant in the health domain which has a large volume of available resources to transform areas important for society when addressing various health research challenges. However, knowledge discovery is usually aided by annotated corpora, which are scarce resources in the literature. This work considers as a start point existent health-oriented Spanish dataset. In addition, it also creates an English variant using the same tagging system. Furthermore, we design and analyze two separated architectures for Entity Extraction and Relation Recognition that outperform previous works in the Spanish dataset. We also evaluate their performance in the English version with such promising results. Finally, we perform a use case experiment to evaluate the utility of the output of these two architectures in Information Retrieval systems.