no code implementations • EMNLP (ClinicalNLP) 2020 • Louise Dupuis, Nicol Bergou, Hegler Tissot, Sumithra Velupillai
Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories.
no code implementations • SEMEVAL 2021 • Raul Almeida, Hegler Tissot, Marcos Didonet Del Fabro
We present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021.
no code implementations • 29 Dec 2019 • Jianyu Liu, Hegler Tissot
We used embedding models trained by a knowledge embedding approach which has been evaluated with clinical datasets.
1 code implementation • 21 Dec 2019 • Matthew Wai Heng Chung, Hegler Tissot
Finally, the correlation between link prediction and classification accuracy shows traditional validation protocol for embedding models is a weak metric to represent the quality of embedding representation.
1 code implementation • WS 2019 • Natalia Viani, Hegler Tissot, Ariane Bernardino, Sumithra Velupillai
To automatically analyse complex trajectory information enclosed in clinical text (e. g. timing of symptoms, duration of treatment), it is important to understand the related temporal aspects, anchoring each event on an absolute point in time.
no code implementations • SEMEVAL 2015 • Hegler Tissot, Genevieve Gorrell, Angus Roberts, Leon Derczynski, Marcos Didonet Del Fabro