Paper: A generative model for unsupervised discovery of relations and argument classes from clinical texts

ACL ID D11-1048
Title A generative model for unsupervised discovery of relations and argument classes from clinical texts
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

This paper presents a generative model for the automatic discovery of relations between entities in electronic medical records. The model discovers relation instances and their types by determining which context tokens ex- press the relation. Additionally, the valid se- mantic classes for each type of relation are de- termined. We show that the model produces clusters of relation trigger words which bet- ter correspond with manually annotated re- lations than several existing clustering tech- niques. The discovered relations reveal some of the implicit semantic structure present in patient records.