This paper describes the WHU IRLAB participation to the Vital Filtering task of the TREC 2014 Knowledge Base Acceleration Track. In this task, we implemented a system to detect vital documents that could be used for a human editor to update or create the profile of an entity. Our approach is to view the problem as a classification problem and use Stanford NLP Toolkit to extract necessary information. Various kinds of features are leveraged to classify documents to three classes, i.e. vital, useful and non-useful (garbage or neutral). We submitted four runs using different combinations of features. The results are presented and discussed.