Biomedical entities recognition in Spanish combining word embeddings
Archivos
Fecha
2021-04-22
Autores
Título de la revista
ISSN de la revista
Título del volumen
Editor
Jaén : Universidad de Jaén
Resumen
El reconocimiento de entidades con nombre (NER) es una tarea importante en el campo del
Procesamiento del Lenguaje Natural que se utiliza para extraer conocimiento significativo de los
documentos textuales. El objetivo de NER es identificar trozos de texto que se refieran a entidades
específicas.
En esta tesis pretendemos abordar la tarea de NER en el dominio biomédico y en español. En este
dominio las entidades pueden referirse a nombres de fármacos, síntomas y enfermedades y ofrecen un
conocimiento valioso a los expertos sanitarios. Para ello, proponemos un modelo basado en redes
neuronales y empleamos una combinación de word embeddings. Además, nosotros generamos unos
nuevos embeddings específicos del dominio y del idioma para comprobar su eficacia. Finalmente,
demostramos que la combinación de diferentes word embeddings como entrada a la red neuronal
mejora los resultados del estado de la cuestión en los escenarios aplicados.
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.
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.
Descripción
Palabras clave
Procesamiento del Lenguaje Natural, aprendizaje profundo, representación de palabras, corpus en español, reconocimiento de entidades biomédicas, Natural Language Processing, deep learning, Spanish corpora, biomedical entity recognition, word embeddings
Citación
p.[http://hdl.handle.net/10953/]