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.Materials and Methods: The task organizers provide...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 19; no. 5; pp. 867 - 875 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104360932&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104360932 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Sep2012 vid: 19 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104360932 NLM22707745 2011646703 10.1136/amiajnl-2011-000766 NLM22707745 PMC3422831 104360932 ppf: 867 ppct: 8 formats: tig: atl: Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules. aug: au: Jonnalagadda, Siddhartha Reddy Li, Dingcheng Sohn, Sunghwan Wu, Stephen Tze-Inn Wagholikar, Kavishwar Torii, Manabu Liu, Hongfang affil: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA. sug: subj: Artificial Intelligence Data Mining Methods Decision Support Systems, Clinical Electronic Health Records Natural Language Processing Algorithms Human Probability Semantics Sensitivity and Specificity United States ab: 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.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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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