The paper describes our experiments addressing the SemEval 2014 task on the Analysis of Clinical text. Our approach consists in extending the techniques of NE recognition, based on sequence labelling, to address the special issues of this task, i.e. the presence of overlapping and discontiguous mentions and the requirement to map the mentions to unique identifiers. We explored using supervised methods in combination with word embeddings generated from unannotated data.
UniPi: Recognition of Mentions of Disorders in Clinical Text
ATTARDI, GIUSEPPE;COZZA, VITTORIA;SARTIANO, DANIELE
2014-01-01
Abstract
The paper describes our experiments addressing the SemEval 2014 task on the Analysis of Clinical text. Our approach consists in extending the techniques of NE recognition, based on sequence labelling, to address the special issues of this task, i.e. the presence of overlapping and discontiguous mentions and the requirement to map the mentions to unique identifiers. We explored using supervised methods in combination with word embeddings generated from unannotated data.File in questo prodotto:
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