This work presents an approach to automatically extract expert knowledge from textual data, focusing on land use change dynamics in West Africa. The main objective is to identify relevant text segments from a corpus of scientific articles in English, using automatic language processing and machine learning methods. We compare two supervised classification approaches with an unsupervised approach based on semantic proximity and show their ability to identify relevant information from unstructured data.