Paper: Exploring Semi-Supervised Coreference Resolution of Medical Concepts using Semantic and Temporal Features

ACL ID N12-1091
Title Exploring Semi-Supervised Coreference Resolution of Medical Concepts using Semantic and Temporal Features
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012
Authors

We investigate the task of medical concept coreference resolution in clinical text using two semi-supervised methods, co-training and multi-view learning with posterior regulariza- tion. By extracting semantic and temporal features of medical concepts found in clinical text, we create conditionally independent data views; co-training MaxEnt classifiers on this data works almost as well as supervised learn- ing for the task of pairwise coreference resolu- tion of medical concepts. We also train Max- Ent models with expectation constraints, using posterior regularization, and find that poste- rior regularization performs comparably to or slightly better than co-training. We describe the process of semantic and temporal feature extraction and demonstrate our methods on a corpus of case report...