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

Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context