Please use this identifier to cite or link to this item:
http://hdl.handle.net/10609/133708
Title: | Application of NLP to extract biomedical entities from COVID-19 papers |
Author: | Chan, Jin Lung |
Director: | Pairo Castiñeira, Erola |
Tutor: | Prados Carrasco, Ferran |
Abstract: | The project explores the current State-of-the-Art of NLP, researches different biomedical datasets, and applies the SciSpacy library to extract and recognize entities from the CORD-19 dataset, a growing data collection with over 500.000 scientific papers linked to COVID-19. The data extraction code is written in Python and deployed in the Kaggle platform. Different visualization software such as Tableau and Gephi has been used to represent the extracted entities in the post-processing analysis. |
Keywords: | named entity recognition COVID-19 natural processing language |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 6-Jun-2021 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
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jlchanTFM0621memory.pdf | Memory of TFM | 3,38 MB | Adobe PDF | View/Open |
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