Uncertainty language permeates biomedical research and is fundamental for the computer interpretation of unstructured text. And yet, a coherent, cognitive-based theory to interpret Uncertainty language and guide Natural Language Processing is, to our knowledge, non-existing. The aim of our project was therefore to detect and annotate Uncertainty markers ― which play a significant role in building knowledge or beliefs in readers' minds ― in a biomedical research corpus. Our corpus includes 80 manually annotated articles from the British Medical Journal randomly sampled from a 168-year period. Uncertainty markers have been classified according to a theoretical framework based on a combined linguistic and cognitive theory. The corpus was manually annotated according to such principles. We performed preliminary experiments to assess the manually annotated corpus and establish a baseline for the automatic detection of Uncertainty markers. The results of the experiments show that most of the Uncertainty markers can be recognized with good accuracy.

A Corpus of Scientific Biomedical Texts Spanning over 168 Years Annotated for Uncertainty

BONGELLI, RAMONA;CANESTRARI, CARLA;RICCIONI, ILARIA;ZUCZKOWSKI, Andrzej;
2012-01-01

Abstract

Uncertainty language permeates biomedical research and is fundamental for the computer interpretation of unstructured text. And yet, a coherent, cognitive-based theory to interpret Uncertainty language and guide Natural Language Processing is, to our knowledge, non-existing. The aim of our project was therefore to detect and annotate Uncertainty markers ― which play a significant role in building knowledge or beliefs in readers' minds ― in a biomedical research corpus. Our corpus includes 80 manually annotated articles from the British Medical Journal randomly sampled from a 168-year period. Uncertainty markers have been classified according to a theoretical framework based on a combined linguistic and cognitive theory. The corpus was manually annotated according to such principles. We performed preliminary experiments to assess the manually annotated corpus and establish a baseline for the automatic detection of Uncertainty markers. The results of the experiments show that most of the Uncertainty markers can be recognized with good accuracy.
2012
9782951740877
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11393/110808
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