Biomedical entities recognition in Spanish combining word embeddings
- Luis Alfonso Ureña López Director
- María Teresa Martín Valdivia Codirectora
- Manuel Carlos Díaz Galiano Codirector
Universitat de defensa: Universidad de Jaén
Data de defensa: 22 de d’abril de 2021
- Rafael Muñoz Guillena President/a
- Paloma Martínez Fernández Secretari/ària
- Manuel Montes Gomez Vocal
Tipus: Tesi
Resum
Named Entity Recognition (NER) is an important task in the field of Natural Language Processing that is used to extract meaningful knowledge from textual documents. The goal of NER is to identify text fragments that refer to specific entities. In this thesis we aim to address the task of NER in the Spanish biomedical domain. In this domain entities can refer to drug, symptom and disease names and offer valuable knowledge to health experts. For this purpose, we propose a model based on neural networks and employ a combination of word embeddings. In addition, we generate new domain- and language-specific embeddings to test their effectiveness. Finally, we show that the combination of different word embeddings as input to the neural network improves the state-of-the-art results in the applied scenarios.