  Word embeddings have been shown adept at capturing the semantic and syntactic
regularities of the natural language text, as a result of which these
representations have found their utility in a wide variety of downstream
content analysis tasks. Commonly, these word embedding techniques derive the
distributed representation of words based on the local context information.
However, such approaches ignore the rich amount of explicit information present
in knowledge-bases. This is problematic, as it might lead to poor
representation for words with insufficient local context such as domain
specific words. Furthermore, the problem becomes pronounced in domain such as
bio-medicine where the presence of these domain specific words are relatively
high. Towards this end, in this project, we propose a new word embedding based
model for biomedical domain that jointly leverages the information from
available corpora and domain knowledge in order to generate knowledge-base
powered embeddings. Unlike existing approaches, the proposed methodology is
simple but adept at capturing the precise knowledge available in domain
resources in an accurate way. Experimental results on biomedical concept
similarity and relatedness task validates the effectiveness of the proposed
approach.
