An important task in philosophy is reading and analyzing texts to identify concepts. The objective of this study is to explore the possibility of a computational support for this task. A classic method is the concordancer, but this approach does not make it possible to distinguish the excerpts where the concept is not expressed in a canonical way. We propose a method to identify these excerpts that we apply to a corpus of articles from the journal Philosophiques. We first determine the excerpts where the concept is expressed explicitly. Then, we identify the excerpts least likely to express the target concept. Finally, we train several classifiers to distinguish the excerpts where the concept is implicitly expressed. The results show a significant difference between the most efficient classifiers, support vector machines and neural networks, and some classical probabilistic models.