We propose vocabulary expansion for video semantic indexing. From many semantic concept detectors obtained by using training data, we make detectors for concepts not included in training data. First, we introduce Mikolov's word vectors to represent a word by a low-dimensional vector. Second, we represent a new concept by a weighted sum of concepts in training data in the word vector space. Finally, we use the same weighting coefficients for combining detectors to make a new detector. In our experiments, we evaluate our methods on the TRECVID Video Semantic Indexing (SIN) Task. We train our models with Google News text documents and ImageNET images to generate new semantic detectors for SIN task. We show that our method performs as well as SVMs trained with 100 TRECVID example videos.