Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules.
OBJECTIVE
This paper describes the coreference resolution system submitted by Mayo Clinic for the 2011 i2b2/VA/Cincinnati shared task Track 1C. The goal of the task was to construct a system that links the markables corresponding to the same entity.


METHODS
The task organizers provided progress notes and discharge summaries that were annotated with the markables of treatment, problem, test, person, and pronoun. We used a multi-pass sieve algorithm that applies deterministic rules in the order of preciseness and simultaneously gathers information about the entities in the documents. Our system, MedCoref, also uses a state-of-the-art machine learning framework as an alternative to the final, rule-based pronoun resolution sieve.


RESULTS
The best system that uses a multi-pass sieve has an overall score of 0.836 (average of B(3), MUC, Blanc, and CEAF F score) for the training set and 0.843 for the test set.


CONCLUSIONS
A supervised machine learning system that typically uses a single function to find coreferents cannot accommodate irregularities encountered in data especially given the insufficient number of examples. On the other hand, a completely deterministic system could lead to a decrease in recall (sensitivity) when the rules are not exhaustive. The sieve-based framework allows one to combine reliable machine learning components with rules designed by experts.


CONCLUSIONS
Using relatively simple rules, part-of-speech information, and semantic type properties, an effective coreference resolution system could be designed. The source code of the system described is available at https://sourceforge.net/projects/ohnlp/files/MedCoref.
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA. siddhartha@mayo.edu
Siddhartha Reddy
SR
Jonnalagadda
Dingcheng
D
Li
Sunghwan
S
Sohn
Stephen Tze-Inn
ST
Wu
Kavishwar
K
Wagholikar
Manabu
M
Torii
Hongfang
H
Liu
D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D057286:Electronic Health Records; D006801:Humans; D008390:Markov Chains; D009323:Natural Language Processing; D012660:Semantics; D012680:Sensitivity and Specificity; D014481:United States