TY  - CONF
ID  - loza16medsim
T1  - Medical Concept Embeddings via Labeled Background Corpora
A1  - Loza Mencía, Eneldo
A1  - de Melo, Gerard
A1  - Nam, Jinseok
TI  - Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)
Y1  - 2016
SP  - 4629
EP  - 4636
PB  - European Language Resources Association (ELRA)
AD  - Paris, France
SN  - 978-2-9517408-9-1
UR  - http://www.lrec-conf.org/proceedings/lrec2016/pdf/1190_Paper.pdf
N2  - In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things,
these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations
of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts,
based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such
as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity
and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that
our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures.
ER  -