Abstract
Biomedical concepts are often mentioned in
medical documents under different name variations (synonyms). This mismatch between
surface forms is problematic, resulting in dif-
ficulties pertaining to learning effective representations. Consequently, this has tremendous implications such as rendering downstream applications inefficacious and/or potentially unreliable. This paper proposes a new
framework for learning robust representations
of biomedical names and terms. The idea
behind our approach is to consider and encode contextual meaning, conceptual meaning, and the similarity between synonyms during the representation learning process. Via
extensive experiments, we show that our proposed method outperforms other baselines on
a battery of retrieval, similarity and relatedness
benchmarks. Moreover, our proposed method
is also able to compute meaningful representations for unseen names, resulting in high practical utility in real-world applications.