Biomedical entities recognition in Spanish combining word embeddings

  1. LÓPEZ ÚBEDA, PILAR
Zuzendaria:
  1. Luis Alfonso Ureña López Zuzendaria
  2. María Teresa Martín Valdivia Zuzendarikidea
  3. Manuel Carlos Díaz Galiano Zuzendarikidea

Defentsa unibertsitatea: Universidad de Jaén

Fecha de defensa: 2021(e)ko apirila-(a)k 22

Epaimahaia:
  1. Rafael Muñoz Guillena Presidentea
  2. Paloma Martínez Fernández Idazkaria
  3. Manuel Montes Gomez Kidea
Saila:
  1. INFORMÁTICA

Mota: Tesia

Teseo: 665953 DIALNET lock_openRUJA editor

Garapen Iraunkorreko Helburuak

Laburpena

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.