Machine learning techniques have become one of the fundamental strategies in any natural language modelling process, also in cases of automatic content processing. Through these techniques, algorithms allow computers to learn from experience. This experience is embodied in training data (datasets), which in the case of supervised learning require prior annotation. The success of the predictions made by the language model depends directly on the quality and size of our training data, especially when used in deep learning algorithms. One of the most relevant advantages of this type of algorithms is that they do not need an a priori design of features. However, this property makes them dependent on larger datasets than traditional machine learning algorithms. In this task we will focus on the construction of annotated datasets with advanced semantic features from previously extracted, compiled and curated collections of resources. This will be done using tools to access other advanced resources such as open databases, and other reusable and redistributable information collections, as well as specific annotation tools for the different use cases. In addition, the quality of the resources will be assured through the development of problem-specific annotation guidelines and annotation quality metrics. Moreover, due to the difficulty of determining certain types of annotation, in many cases of complex characterisation even that done by human experts, research will be carried out on the generation of datasets in a semi-automatic way, so that the creation of corpora reduces in cost considerably and the increase of training examples improves the accuracy of the machine learning and deep learning methods used.