A classification approach to coreference in discharge summaries: 2011 i2b2 challenge
Journal of the American Medical Informatics Association, 2012•academic.oup.com
Objective To create a highly accurate coreference system in discharge summaries for the
2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and
Test. Design An integrated coreference resolution system was developed by exploiting
Person attributes, contextual semantic clues, and world knowledge. It includes three
subsystems: Person coreference system based on three Person attributes,
Problem/Treatment/Test system based on numerous contextual semantic extractors and …
2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and
Test. Design An integrated coreference resolution system was developed by exploiting
Person attributes, contextual semantic clues, and world knowledge. It includes three
subsystems: Person coreference system based on three Person attributes,
Problem/Treatment/Test system based on numerous contextual semantic extractors and …
Abstract
Objective To create a highly accurate coreference system in discharge summaries for the 2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and Test.
Design An integrated coreference resolution system was developed by exploiting Person attributes, contextual semantic clues, and world knowledge. It includes three subsystems: Person coreference system based on three Person attributes, Problem/Treatment/Test system based on numerous contextual semantic extractors and world knowledge, and Pronoun system based on a multi-class support vector machine classifier. The three Person attributes are patient, relative and hospital personnel. Contextual semantic extractors include anatomy, position, medication, indicator, temporal, spatial, section, modifier, equipment, operation, and assertion. The world knowledge is extracted from external resources such as Wikipedia.
Measurements Micro-averaged precision, recall and F-measure in MUC, BCubed and CEAF were used to evaluate results.
Results The system achieved an overall micro-averaged precision, recall and F-measure of 0.906, 0.925, and 0.915, respectively, on test data (from four hospitals) released by the challenge organizers. It achieved a precision, recall and F-measure of 0.905, 0.920 and 0.913, respectively, on test data without Pittsburgh data. We ranked the first out of 20 competing teams. Among the four sub-tasks on Person, Problem, Treatment, and Test, the highest F-measure was seen for Person coreference.
Conclusions This system achieved encouraging results. The Person system can determine whether personal pronouns and proper names are coreferent or not. The Problem/Treatment/Test system benefits from both world knowledge in evaluating the similarity of two mentions and contextual semantic extractors in identifying semantic clues. The Pronoun system can automatically detect whether a Pronoun mention is coreferent to that of the other four types. This study demonstrates that it is feasible to accomplish the coreference task in discharge summaries.
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