Abstract
This paper describes VUACLTL, the system the CLTL Lab submitted to the SemEval 2016 Task Clinical TempEval. The system is based on a purely data-driven approach based on a cascade of seven CRF classifiers which use
generic features and little domain knowledge. The challenge consisted in six subtasks related to temporal processing clinical notes from raw text (event and temporal expression detection and attribute classification, temporal relation
classification between events and the Document Creation Time, and narrative container detection). The system was initially developed to process newswire texts and then re-trained to process clinical notes. This had an impact on the results, which are not equally competitive for all the subtasks.
generic features and little domain knowledge. The challenge consisted in six subtasks related to temporal processing clinical notes from raw text (event and temporal expression detection and attribute classification, temporal relation
classification between events and the Document Creation Time, and narrative container detection). The system was initially developed to process newswire texts and then re-trained to process clinical notes. This had an impact on the results, which are not equally competitive for all the subtasks.
Original language | English |
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Title of host publication | Proceedings of SemEval-2016 |
Place of Publication | San Diego, California |
Publisher | Association for Computational Linguistics |
Pages | 1241-1247 |
ISBN (Print) | 9781941643952 |
Publication status | Published - 2016 |
Event | SemEval-2016 - San Diego, California Duration: 1 Jan 2016 → 1 Jan 2016 |
Conference
Conference | SemEval-2016 |
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Period | 1/01/16 → 1/01/16 |