In 2019, around 1.4 billion e-mails are sent every day in France (293 billion worldwide). E-mails significantly increase the volume of communications in companies. As a result, it is difficult for employees to read all messages in order to identify the tasks to be carried out. First systems to identify tasks in e-mails appeared at the end of the 1990s. Much work has been done on this topic, based on machine learning, symbolic methods, and hybrid methods. Two approaches are commonly adopted: 1) classification of language acts (at the message level or sentence level, 2) information extraction based on linguistic patterns. We propose and experiment with an approach based on event extraction (from Information Extraction) and Semantic Role Labeling to identify and structure tasks in emails. The evaluation of our system on professional e-mails shows the relevance of our proposal.