Automated detection of discourse segment and experimental types from the text of cancer pathway results sections.

ABSTRACT: Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles' Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may

SUBMITTER: Burns GA 

PROVIDER: S-EPMC5006090 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature