J Am Med Inform Assoc - Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules.

Tópicos

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Resumo

JECTIVE: 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.MATERIALS AND 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.DISCUSSION: 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.CONCLUSION: 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.

Resumo Limpo

jectiv paper describ corefer resolut system submit mayo clinic ibvacincinnati share task track c goal task construct system link markabl correspond entitymateri method task organ provid progress note discharg summari annot markabl treatment problem test person pronoun use multipass siev algorithm appli determinist rule order precis simultan gather inform entiti document system medcoref also use stateoftheart machin learn framework altern final rulebas pronoun resolut sieveresult best system use multipass siev overal score averag b muc blanc ceaf f score train set test setdiscuss supervis machin learn system typic use singl function find corefer accommod irregular encount data especi given insuffici number exampl hand complet determinist system lead decreas recal sensit rule exhaust sievebas framework allow one combin reliabl machin learn compon rule design expertsconclus use relat simpl rule partofspeech inform semant type properti effect corefer resolut system design sourc code system describ avail httpssourceforgenetprojectsohnlpfilesmedcoref

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