  Medical concept normalization helps in discovering standard concepts in
free-form text i.e., maps health-related mentions to standard concepts in a
vocabulary. It is much beyond simple string matching and requires a deep
semantic understanding of concept mentions. Recent research approach concept
normalization as either text classification or text matching. The main drawback
in existing a) text classification approaches is ignoring valuable target
concepts information in learning input concept mention representation b) text
matching approach is the need to separately generate target concept embeddings
which is time and resource consuming. Our proposed model overcomes these
drawbacks by jointly learning the representations of input concept mention and
target concepts. First, it learns the input concept mention representation
using RoBERTa. Second, it finds cosine similarity between embeddings of input
concept mention and all the target concepts. Here, embeddings of target
concepts are randomly initialized and then updated during training. Finally,
the target concept with maximum cosine similarity is assigned to the input
concept mention. Our model surpasses all the existing methods across three
standard datasets by improving accuracy up to 2.31%.

Computation and Language