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 chunks of text that refer to specific entities. In this thesis, we aim to address the task of NER in the biomedical domain and in Spanish. In this domain, entities can refer to names of drugs, symptoms, and diseases 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.