Term dependency models are generally better than bag-of-word models, because complete concepts are often represented by multiple terms. However, without semantic knowledge, such models may introduce many false dependencies among terms, especially when the document collection is small and homogeneous(e.g. newswire documents, medical documents). The main contribution of this work is to incorporate semantic knowledge with term dependency models, so that more accurate dependency relations will be assigned to terms in the query. In this paper, experiments will be made on CLEF2013 eHealth Lab medical information retrieval data set, and the baseline term dependency model will be the popular MRF(Markov Random Field) model [
], which proves to be better than traditional independent models in general domain search. Experiment results show that, in medical document retrieval, full dependency MRF model is worse than independent model, it can be significantly improved by incorporating semantic knowledge.