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UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text
2016 (engelsk)Inngår i: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), Association for Computational Linguistics , 2016, 1256-1262 s.Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The 2016 Clinical TempEval continued the 2015 shared task on temporal information extraction with a new evaluation test set. Our team, UtahBMI, participated in all subtasks using machine learning approaches with ClearTK (LIBLINEAR), CRF++ and CRFsuite packages. Our experiments show that CRF-based classifiers yield, in general, higher recall for multi-word spans, while SVM-based classifiers are better at predicting correct attributes of TIMEX3. In addition, we show that an ensemble-based approach for TIMEX3 could yield improved results. Our team achieved competitive results in each subtask with an F1 75.4% for TIMEX3, F1 89.2% for EVENT, F1 84.4% for event relations with document time (DocTimeRel), and F1 51.1% for narrative container (CONTAINS) relations.

sted, utgiver, år, opplag, sider
Association for Computational Linguistics , 2016. 1256-1262 s.
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-204780OAI: oai:DiVA.org:kth-204780DiVA: diva2:1086116
Konferanse
10th International Workshop on Semantic Evaluation (SemEval-2016)
Merknad

QC 20170418

Tilgjengelig fra: 2017-03-31 Laget: 2017-03-31 Sist oppdatert: 2017-04-18bibliografisk kontrollert

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http://www.aclweb.org/anthology/S16-1195

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Velupillai, Sumithra

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Totalt: 81 treff
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