This task will identify different tools and algorithms that can be used to develop the software necessary to achieve the goals of the project. Taking into account the state-of-the-art, we will analyze different open source libraries in specific programming languages for the development and evaluation of machine learning models. Regarding machine learning, different algorithms to develop predictive models will be analyzed. Including for example, SVM, Random Forest or k-NN. These algorithms could be complemented with other approaches like for example deep learning architectures, including fine-tunning over semisupervised models or zero-shot learning solutions. These models could help to develop algorithms based on deep neural networks such as RNN and LSTM. In order to support this kind of implementation we will analyze different libraries like for example TensorFlow, Pytorch and Keras. In order to perform text analysis, it will be necessary to use specific NLP libraries. We will analyze different libraries to perform linguistic analysis, support different languages, provide name entity recognition, sentiment analysis or entity linking, among other possibilities. Additionally, we will try to incorporate Transformer models . These models will provide dense vector representations for words in a semantic space that can be further used in downstream NLP applications such as text classification, questions answering and entity recognition. In recent years, the use of Transformers has led to performance improvements in many NLP tasks.