  Making sense of words often requires to simultaneously examine the
surrounding context of a term as well as the global themes characterizing the
overall corpus. Several topic models have already exploited word embeddings to
recognize local context, however, it has been weakly combined with the global
context during the topic inference. This paper proposes to extract topical
phrases corroborating the word embedding information with the global context
detected by Latent Semantic Analysis, and then combine them by means of the
P\'{o}lya urn model. To highlight the effectiveness of this combined approach
the model was assessed analyzing clinical reports, a challenging scenario
characterized by technical jargon and a limited word statistics available.
Results show it outperforms the state-of-the-art approaches in terms of both
topic coherence and computational cost.
