This thesis reports on the development process of PetroGold, a goldstandard annotated corpus with morphosyntactic information – a treebank – for the oil and gas domain. The development of the resource is seen from two perspectives: on the linguistic side, we study the grammatical literature and make linguistically motivated decisions to ensure the quality of corpus annotation; on the computational side, we evaluate the resource considering its usefulness for natural language processing (NLP). Resources like PetroGold receive special importance in the current context, where statistical NLP has benefited from domain-specific gold-standard resources to train machine learning models. However, the treebank is also useful for tasks such as evaluating rule-based annotation systems and for linguistic studies. PetroGold was annotated according to the guidelines of the Universal Dependencies project, having as theoretical assumptions the idea that the annotation of a corpus is an interpretative process, on the one hand, and using the empirical linguistics paradigm, on the other. In addition to describing the annotation itself, we apply some methods to find errors in the annotation of treebanks and present a tool created specifically for searching, editing and evaluating annotated corpora. Finally, we evaluate the impact of revising each of the treebank linguistic categories on the automatic learning of a model powered by PetroGold and make the third version of the corpus publicly available, which, when performing an intrinsic evaluation for a model using the corpus, achieves metrics up to 2.55 perecent better than the previous version.