Knowledge Enhanced Representations to Reduce the Semantic Gap in Clinical Decision Support

Stefano Marchesin
2019 BCS-IRSG Symposium on Future Directions in Information Access  
The semantic gap between queries and documents is a longstanding problem in Information Retrieval (IR), and it poses a critical challenge for medical IR due to the large presence in the medical language of synonymous and polysemous words, along with context-specific expressions. Two main lines of work have emerged in the past years to tackle this issue: (i) the use of external knowledge resources to enhance query and document bag-of-words representations; and (ii) the use of semantic models,
more » ... ed on the distributional hypothesis, which perform matching on latent representations of documents and queries. The presented research investigates the use of external knowledge resources in both lines -with a focus on knowledge-enhanced unsupervised neural latent representations and their analysis in terms of effectiveness and semantic representativeness.
dblp:conf/fdia/Marchesin19 fatcat:5cj3rjszavd6pa3g6tklppenaq