UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text
2016 (English)In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), Association for Computational Linguistics , 2016, 1256-1262 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
Association for Computational Linguistics , 2016. 1256-1262 p.
Language Technology (Computational Linguistics)
IdentifiersURN: urn:nbn:se:kth:diva-204780OAI: oai:DiVA.org:kth-204780DiVA: diva2:1086116
10th International Workshop on Semantic Evaluation (SemEval-2016)
QC 201704182017-03-312017-03-312017-04-18Bibliographically approved