As the persistent COVID-19 outbreak transforms the world, scientists are vigorously researching the virus to find a cure. For researchers to stay updated with the latest developments, they have to find the time to evaluate current advancements and potentially apply them in their research. However, because of the global initiative to solve this crisis, new research is getting published continually. That causes an abundance of research papers, where most of them do not deliver any significance to the on-going cure development. To help researchers find the most prominent improvements, we propose the solution in the form of an information retrieval model, that we adapted to the scientific domain of this problem. Our model will allow researchers to find the most relevant papers for each of their COVID-19 related queries, and in the process, save some valuable time.
COVID19 Information Model is a University Project for a course Text analysis and retrieval. Our task was to build an information retrieval model that would scan and obtain papers that are most related to the given query.
Our final model is placed in the final-model.ipynb notebook. To use it, open the file and follow the instructions.
This project is licensed under the MIT License - see the LICENSE.md file for details