Negation and speculation processing: A study on cue-scope labelling and assertion classification in Spanish clinical text
Authors: Naiara Perez Miguel Germán Rigau
Date: 01.11.2023
Artificial Intelligence in Medicine
Abstract
Natural Language Processing (NLP) based on new deep learning technology is contributing to the emergence of powerful solutions that help healthcare providers and researchers discover valuable patterns within insurmountable volumes of health records and scientific literature. Fundamental to the success of such solutions is the processing of negation and speculation. The article addresses this problem with state-of-the-art deep learning approaches from two perspectives: cue and scope labelling, and assertion classification. In light of the real struggle to access clinical annotated data, the study (a) proposes a methodology to automatically convert cue-scope annotations to assertion annotations; and (b) includes a range of scenarios with varying amounts of training data and adversarial test examples. The results expose the clear advantage of Transformer-based models in this regard, managing to overpass a series of baselines and the related work in the public corpus NUBes of clinical Spanish text.
BIB_text
title = {Negation and speculation processing: A study on cue-scope labelling and assertion classification in Spanish clinical text},
journal = {Artificial Intelligence in Medicine},
pages = {102682},
volume = {145},
keywds = {
Biomedical language processing; Deep learning; Natural Language Processing; Negation; Speculation
}
abstract = {
Natural Language Processing (NLP) based on new deep learning technology is contributing to the emergence of powerful solutions that help healthcare providers and researchers discover valuable patterns within insurmountable volumes of health records and scientific literature. Fundamental to the success of such solutions is the processing of negation and speculation. The article addresses this problem with state-of-the-art deep learning approaches from two perspectives: cue and scope labelling, and assertion classification. In light of the real struggle to access clinical annotated data, the study (a) proposes a methodology to automatically convert cue-scope annotations to assertion annotations; and (b) includes a range of scenarios with varying amounts of training data and adversarial test examples. The results expose the clear advantage of Transformer-based models in this regard, managing to overpass a series of baselines and the related work in the public corpus NUBes of clinical Spanish text.
}
doi = {10.1016/j.artmed.2023.102682},
date = {2023-11-01},
}
