This paper presents a semantic classifier working from text transcriptions coming from automatic speech recognition (ASR). The system is based on a Bayesian classifier, it is fully linked with a knowledge base which contains an ontology and named entities from several domains. The system is trained from a set of positive and negative examples for each indexed concept. It has been evaluated using the TREC VIDEO protocol and conditions for the detection of visual concepts. Three versions are compared: a baseline one, using only word as units, a second, using additionally named entities, and a last one using semantic classes.